import pandas as pd
We have created a new dataframe from scraped data to avoid the deliberate errors included in the provided dataset and checked it against the plots on the sharedprosperity site. The process of creating and checking this dataframe and imputing missing values is detailed later in this document. First we will look at some of the findings that can help guide further analysis.
So this is supposed to be structured as a report; It's a progress report. So we're not up to the point of presenting complete conclusions but I've gotten as far as I can without sacrificing another exemption day.
What we've got so far is a dataframe that's usable and hopefully as accurate as you could make it and then we've got an imputed dataframe using one method;
Apart from that we've got a bunch of factor analysis and component analysis output and some correlation matrices, some pair plots; the general gist of it is that a few factors seem to explain most of this data, that many of these variables are highly correlated; obviously the variables within the dimensions identified by the shared prosperity project are highly correlated with one another.
What else? So we are supposed to take a different direction from that shown in the project online which is a set of eight dimensions so the way I would propose to do something different is by looking at relationships between those dimensions but also looking at smaller numbers of factors or dimensions or variables.
It looks like we can go as low as one, pretty much inequality.
The take-away from all this is that there's a whole lot more work to be done but this one single factor has been getting worse the whole time and everything else comes from it. It's not just financial but all these other factors too.
The closest thing to findings that we have here come in the form of the following plot; apart from the general idea that there are a small number of factors, possibly only one or two that explain all this, there are some factors which seem to be beneficial; high among these is middle class income share and various factors about education, but this could all change if we tried different imputations or factor analysis so we'll see.
Below is a scree plot which shows the strength of derived significant factors:
We also have here some pair plots from an abortive attempt to create like petabytes worth of them from a poorly thought out command
Marks for presentation said not too many dataframe dumps but i'll do one each here of my scraped dataframe and the imputed one since i'm only giving you this notebook and not any of the csv files ive got in my virtual machine's file system i've been using to do this. Unfortunately I've been running a chromebook the last few weeks because my laptop's away getting fixed so this whole thing has been done on the virtual machine and I've been using its filesystem as an extension of the jupyter interface pretty much.
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
new_df
| Unnamed: 0 | Q5 to Q1 income share ratio | D10 to D1 income share ratio | D10 to D1-4 income share ratio (Palma) | P90 to P10 income ratio | P80 to P20 income ratio | P80 to P50 income ratio | P50 to P20 income ratio | GINI | Top 10 percent wealth share | Top 5 percent wealth share | Top 1 percent wealth share | Lower deciles income share | Middle class income share | Unemployment rate | Unemployment rate, 60-64 | Unemployment rate, 65+ | Underemployment rate | Percentage of employees working long hours | Labour market insecurity | Long-term unemployment rate | Percentage of youth NEET | Percentage of workforce on low pay, OECD definition | Percentage of workforce on low pay, relative to minimum wage | Minimum to living wage gap | Labour share of income | Labour productivity to real product wages ratio | Percentage of households spending above 30% of income on rental | Percentage of households spending above 30% of income on housing | House affordability, rent | House affordability, purchase | Percentage of home ownership | Percentage of homelessness in population | Percentage of Priority A state housing applicants in population | Percentage of Priority B state housing applicants in population | Percentage of disposable income spent on household debt | Median multiple for housing | Health expenditure as a percentage of GDP | Health expenditure per capita, PPP | Prevalence of depression, adult | Prevalence of self-rated health as good or better, adult | Prevalence of pyschological distress, adult | Prevalence of mood_anxiety disorders, adult | Prevalence of healthy weight, adult | Prevalence of unmet need for after-hours care due to cost, adult | Prevalence of unmet need for GP due to cost, adult | Prevalence of adequate vegetable and fruit intake, adult | Prevalence of breakfasting at home less than 5 days, child | Prevalence of emotional_behavioural problems, child | Prevalence of diabetes, adult | Prevalence of depression, child | Prevalence of good or better parent-rated health, child | Prevalence of unfulfilled prescriptions due to cost, child | Prevalence of unmet need to after hours care due to cost, child | Prevalence of unmet need for GP due to cost, child | Prevalence of adequate vegetable and fruit intake, child | Prevalence of healthy weight, child | Rate of suicides | Prevalence of problem gambling interventions | Prevalence of poverty 60% ML | Prevalence of poverty 50% ML | Prevalence of poverty 50% AL | Prevalence of poverty 60% AL | Prevalence of poverty 40% AL | Prevalence of poverty, 60% ML, elderly | Prevalence of poverty 50% ML, elderly | Prevalence of poverty 50% AL, elderly | Prevalence of poverty 60% AL, elderly | Poverty risk ratio, 60% AL, single under 65 | Poverty risk ratio, 60% AL, solo parent | Poverty risk ratio, 50% AL, single under 65 | Poverty risk ratio, 50% AL, solo parent | Prevalence of poverty, 50% AL, child | Prevalence of poverty, 60% AL, child | Prevalence of poverty, 40% ML, child | Prevalence of poverty, 50% ML, child | Prevalence of poverty, 60% ML, child | Prevalence of poverty, 60% AL, children with part time working parent_s | Prevalence of poverty, 60% AL, children with full time working parent_s | Prevalence of personal insolvencies | Loan delinquencies | Tertiary education participation | Education expenditure, GDP | Education expenditure, government expenses | Tertiary loan as a percentage of income | Tertiary loan leaving balance as a percentage of income | University fees to income ratio | Polytechnic fees to income ratio | Wānanga fees to income ratio | Degree earnings premium, hourly | Diploma_certificate earnings premium, hourly | School earnings premium, hourly | Degree earnings premium, weekly | Diploma_certificate earnings premium, weekly | School earnings premium, weekly | Percentage of population in remand | Percentage of population sentenced | Percentage of population post-sentence | Incidence of crime victimisation | Rate of murder and homicide | Regional GDP variation | Regional income inadequacy variation | Inadequacy Of Income, Gender | Inadequacy Of Income, Housing Tenure | Inadequacy Of Income, Long-Term Migrant | Inadequacy Of Income, Māori | Low Income, Gender | Gender pay gap | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1982 | 4.110000 | 6.120000 | 0.910000 | 3.260000 | 2.300000 | 1.510000 | 0.660000 | 27.200000 | NaN | NaN | NaN | 22.200000 | 63.300000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.000000 | 9.000000 | 12.000000 | NaN | 6.000000 | 5.000000 | 2.000000 | 4.000000 | NaN | NaN | NaN | NaN | NaN | 18.000000 | NaN | 8.000000 | 13.000000 | 21.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1983 | 4.125000 | 6.125000 | 0.920000 | 3.285000 | 2.305000 | 1.525000 | 0.665000 | 27.350000 | NaN | NaN | NaN | 22.250000 | 62.900000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.000000 | 8.500000 | 12.500000 | NaN | 5.500000 | 4.000000 | 2.000000 | 3.500000 | NaN | NaN | NaN | NaN | NaN | 19.500000 | NaN | 8.000000 | 13.500000 | 21.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 1984 | 4.140000 | 6.130000 | 0.930000 | 3.310000 | 2.310000 | 1.540000 | 0.670000 | 27.500000 | NaN | NaN | NaN | 22.300000 | 62.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.000000 | 8.000000 | 13.000000 | NaN | 5.000000 | 3.000000 | 2.000000 | 3.000000 | NaN | NaN | NaN | NaN | NaN | 21.000000 | NaN | 8.000000 | 14.000000 | 22.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 1985 | 4.090000 | 6.085000 | 0.925000 | 3.240000 | 2.265000 | 1.520000 | 0.675000 | 27.250000 | NaN | NaN | NaN | 22.550000 | 63.550000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.332 | NaN | 13.500000 | 8.000000 | 12.000000 | NaN | 4.500000 | 4.500000 | 2.000000 | 3.000000 | NaN | NaN | NaN | NaN | NaN | 19.000000 | NaN | 7.000000 | 13.000000 | 21.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 1986 | 4.040000 | 6.040000 | 0.920000 | 3.170000 | 2.220000 | 1.500000 | 0.680000 | 27.000000 | NaN | NaN | NaN | 22.800000 | 64.600000 | 4.2 | 1.3 | 2.00 | NaN | NaN | NaN | 0.331 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.634 | NaN | 13.000000 | 8.000000 | 11.000000 | NaN | 4.000000 | 6.000000 | 2.000000 | 3.000000 | NaN | NaN | NaN | NaN | NaN | 17.000000 | NaN | 6.000000 | 12.000000 | 21.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 5 | 1987 | 4.045000 | 6.070000 | 0.915000 | 3.145000 | 2.220000 | 1.505000 | 0.680000 | 27.050000 | NaN | NaN | NaN | 22.600000 | 64.150000 | 4.2 | 1.3 | 1.70 | NaN | NaN | NaN | 0.446 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.015 | NaN | 13.500000 | 8.000000 | 11.500000 | NaN | 4.500000 | 6.000000 | 2.000000 | 3.500000 | NaN | NaN | NaN | NaN | NaN | 17.000000 | NaN | 6.500000 | 12.000000 | 21.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 6 | 1988 | 4.050000 | 6.100000 | 0.910000 | 3.120000 | 2.220000 | 1.510000 | 0.680000 | 27.100000 | NaN | NaN | NaN | 22.400000 | 63.700000 | 5.8 | 2.3 | 2.30 | NaN | NaN | NaN | 0.782 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.591 | NaN | 14.000000 | 8.000000 | 12.000000 | NaN | 5.000000 | 6.000000 | 2.000000 | 4.000000 | NaN | NaN | NaN | NaN | NaN | 17.000000 | NaN | 7.000000 | 12.000000 | 21.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN |
| 7 | 1989 | 4.240000 | 6.245000 | 1.005000 | 3.285000 | 2.305000 | 1.560000 | 0.675000 | 28.650000 | NaN | NaN | NaN | 22.100000 | 61.900000 | 7.3 | 2.3 | 3.40 | NaN | NaN | NaN | 1.269 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.963 | NaN | 14.500000 | 8.000000 | 12.500000 | NaN | 4.500000 | 6.000000 | 2.000000 | 3.500000 | NaN | NaN | NaN | NaN | NaN | 18.500000 | NaN | 6.500000 | 12.500000 | 21.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN |
| 8 | 1990 | 4.430000 | 6.390000 | 1.100000 | 3.450000 | 2.390000 | 1.610000 | 0.670000 | 30.200000 | NaN | NaN | NaN | 21.300000 | 59.500000 | 8.0 | 3.3 | 2.10 | NaN | NaN | NaN | 1.760 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.532 | NaN | 15.000000 | 8.000000 | 13.000000 | NaN | 4.000000 | 6.000000 | 2.000000 | 3.000000 | NaN | NaN | NaN | NaN | NaN | 20.000000 | NaN | 6.000000 | 13.000000 | 22.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN |
| 9 | 1991 | 4.765000 | 7.290000 | 1.140000 | 3.645000 | 2.465000 | 1.640000 | 0.665000 | 31.050000 | NaN | NaN | NaN | 20.200000 | 57.600000 | 10.6 | 2.8 | 2.50 | NaN | NaN | NaN | 2.578 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 73.298 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.559 | NaN | 18.000000 | 10.500000 | 18.000000 | NaN | 6.000000 | 4.500000 | 1.500000 | 4.000000 | NaN | NaN | NaN | NaN | NaN | 28.500000 | NaN | 8.500000 | 17.000000 | 27.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.500000 | NaN |
| 10 | 1992 | 5.100000 | 8.190000 | 1.180000 | 3.840000 | 2.540000 | 1.670000 | 0.660000 | 31.900000 | NaN | NaN | NaN | 20.000000 | 56.100000 | 10.7 | 2.7 | 1.70 | NaN | NaN | NaN | 3.448 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 72.666 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.954 | NaN | 21.000000 | 13.000000 | 23.000000 | NaN | 8.000000 | 3.000000 | 1.000000 | 5.000000 | NaN | NaN | NaN | NaN | NaN | 37.000000 | NaN | 11.000000 | 21.000000 | 33.000000 | NaN | NaN | NaN | 6.474 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 | NaN |
| 11 | 1993 | 5.100000 | 8.115000 | 1.195000 | 3.900000 | 2.545000 | 1.675000 | 0.660000 | 32.050000 | NaN | NaN | NaN | 19.700000 | 54.300000 | 9.8 | 2.5 | 1.70 | NaN | NaN | NaN | 3.309 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 72.042 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.396 | NaN | 21.000000 | 13.500000 | 24.500000 | NaN | 8.500000 | 3.000000 | 1.000000 | 5.500000 | NaN | NaN | NaN | NaN | NaN | 39.000000 | NaN | 12.000000 | 21.500000 | 34.000000 | NaN | NaN | NaN | 1.380 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.500000 | NaN |
| 12 | 1994 | 5.100000 | 8.040000 | 1.210000 | 3.960000 | 2.550000 | 1.680000 | 0.660000 | 32.200000 | NaN | NaN | NaN | 19.900000 | 54.900000 | 8.4 | 3.3 | 2.30 | NaN | NaN | NaN | 2.750 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 71.422 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.137 | NaN | 21.000000 | 14.000000 | 26.000000 | NaN | 9.000000 | 3.000000 | 1.000000 | 6.000000 | NaN | NaN | NaN | NaN | NaN | 41.000000 | NaN | 13.000000 | 22.000000 | 35.000000 | NaN | NaN | NaN | -2.502 | 7.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.181 | 17.120 | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 | NaN |
| 13 | 1995 | 5.155000 | 8.260000 | 1.245000 | 3.895000 | 2.550000 | 1.675000 | 0.655000 | 32.650000 | NaN | NaN | NaN | 19.800000 | 56.100000 | 6.5 | 2.7 | 1.50 | NaN | NaN | NaN | 1.667 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 70.790 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.772 | NaN | 20.500000 | 14.000000 | 24.000000 | NaN | 9.000000 | 4.000000 | 2.000000 | 6.000000 | NaN | NaN | NaN | NaN | NaN | 37.500000 | NaN | 13.500000 | 22.000000 | 33.500000 | NaN | NaN | NaN | -0.184 | 8.1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.309 | 10.882 | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 | NaN |
| 14 | 1996 | 5.210000 | 8.480000 | 1.280000 | 3.830000 | 2.550000 | 1.670000 | 0.650000 | 33.100000 | NaN | NaN | NaN | 19.800000 | 55.200000 | 6.3 | 2.9 | 1.40 | NaN | NaN | NaN | 1.333 | NaN | NaN | NaN | NaN | 0.574 | 0.983 | NaN | NaN | NaN | NaN | 70.204 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.462 | NaN | 20.000000 | 14.000000 | 22.000000 | NaN | 9.000000 | 5.000000 | 3.000000 | 6.000000 | NaN | NaN | NaN | NaN | NaN | 34.000000 | NaN | 14.000000 | 22.000000 | 32.000000 | NaN | NaN | NaN | -0.951 | 8.1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.334 | 14.737 | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 | NaN |
| 15 | 1997 | 5.230000 | 8.515000 | 1.280000 | 3.775000 | 2.560000 | 1.655000 | 0.645000 | 33.050000 | NaN | NaN | NaN | 18.900000 | 54.400000 | 6.8 | 3.3 | 1.00 | NaN | NaN | NaN | 1.346 | NaN | 13.270 | NaN | NaN | 0.577 | 0.974 | NaN | NaN | NaN | NaN | 69.633 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 14.858 | NaN | 20.000000 | 14.000000 | 20.500000 | NaN | 9.000000 | 6.000000 | 3.500000 | 6.500000 | NaN | NaN | NaN | NaN | NaN | 32.000000 | NaN | 14.000000 | 21.500000 | 31.500000 | NaN | NaN | NaN | 1.803 | 8.1 | 4.8 | 15.4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.129 | 17.453 | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 | NaN |
| 16 | 1998 | 5.250000 | 8.550000 | 1.280000 | 3.720000 | 2.570000 | 1.640000 | 0.640000 | 33.000000 | NaN | NaN | NaN | 19.600000 | 54.900000 | 7.7 | 3.4 | 1.20 | NaN | NaN | NaN | 1.508 | NaN | 13.121 | NaN | NaN | 0.577 | 0.976 | NaN | NaN | NaN | NaN | 69.066 | NaN | NaN | NaN | 8.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 15.174 | NaN | 20.000000 | 14.000000 | 19.000000 | NaN | 9.000000 | 7.000000 | 4.000000 | 7.000000 | NaN | NaN | NaN | NaN | NaN | 30.000000 | NaN | 14.000000 | 21.000000 | 31.000000 | NaN | NaN | NaN | 3.999 | 8.2 | 4.9 | 15.7 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 7.855 | 13.894 | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 | 16.2 |
| 17 | 1999 | 5.303333 | 8.520000 | 1.300000 | 3.816667 | 2.593333 | 1.653333 | 0.640000 | 33.266667 | NaN | NaN | NaN | 19.466667 | 54.333333 | 7.0 | 5.4 | 1.80 | NaN | NaN | NaN | 1.485 | NaN | 12.284 | NaN | NaN | 0.586 | 0.961 | NaN | NaN | NaN | NaN | 68.497 | NaN | NaN | NaN | 7.7 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.473 | NaN | 21.000000 | 14.333333 | 19.666667 | NaN | 9.000000 | 7.333333 | 4.000000 | 7.000000 | NaN | NaN | NaN | NaN | NaN | 30.666667 | NaN | 13.666667 | 22.000000 | 32.333333 | NaN | NaN | 0.116 | 2.712 | 9.4 | 5.0 | 15.7 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.190 | 0.188 | NaN | 7.342 | 13.038 | NaN | NaN | NaN | NaN | NaN | NaN | 2.333333 | 15.2 |
| 18 | 2000 | 5.356667 | 8.490000 | 1.320000 | 3.913333 | 2.616667 | 1.666667 | 0.640000 | 33.533333 | NaN | NaN | NaN | 19.333333 | 53.766667 | 6.1 | 4.8 | 1.70 | NaN | NaN | NaN | 1.222 | NaN | 11.739 | NaN | NaN | 0.566 | 1.000 | NaN | NaN | NaN | NaN | 67.919 | NaN | NaN | NaN | 9.4 | NaN | 7.5 | 1606.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 11.891 | NaN | 22.000000 | 14.666667 | 20.333333 | NaN | 9.000000 | 7.666667 | 4.000000 | 7.000000 | NaN | NaN | NaN | NaN | NaN | 31.333333 | NaN | 13.333333 | 23.000000 | 33.666667 | NaN | NaN | 0.094 | 2.259 | 10.1 | 5.0 | 16.4 | 30.095 | 46.791 | 5.560 | 5.369 | 3.859 | NaN | NaN | NaN | NaN | NaN | NaN | 0.205 | 0.183 | NaN | 6.980 | 14.518 | NaN | NaN | NaN | NaN | NaN | NaN | 2.666667 | 14.0 |
| 19 | 2001 | 5.410000 | 8.460000 | 1.340000 | 4.010000 | 2.640000 | 1.680000 | 0.640000 | 33.800000 | NaN | NaN | NaN | 19.200000 | 53.200000 | 5.5 | 3.4 | 1.30 | NaN | NaN | NaN | 0.945 | NaN | 12.229 | NaN | NaN | 0.553 | 1.027 | NaN | NaN | NaN | NaN | 67.657 | 0.737000 | NaN | NaN | 8.5 | 3.204 | 7.6 | 1693.8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.070 | NaN | 23.000000 | 15.000000 | 21.000000 | 28.000000 | 9.000000 | 8.000000 | 4.000000 | 7.000000 | NaN | NaN | NaN | NaN | NaN | 32.000000 | 41.000000 | 13.000000 | 24.000000 | 35.000000 | NaN | NaN | 0.100 | 3.110 | 11.0 | 5.1 | 16.8 | 30.483 | 45.867 | 5.552 | 4.960 | 1.794 | NaN | NaN | NaN | NaN | NaN | NaN | 0.224 | 0.186 | NaN | 6.841 | 13.656 | 0.540 | NaN | NaN | NaN | NaN | NaN | 3.000000 | 13.1 |
| 20 | 2002 | 5.453333 | 8.633333 | 1.326667 | 4.063333 | 2.673333 | 1.663333 | 0.626667 | 33.666667 | NaN | NaN | NaN | 19.133333 | 53.666667 | 5.3 | 4.0 | 1.70 | NaN | NaN | NaN | 0.782 | NaN | 13.630 | NaN | NaN | 0.547 | 1.037 | NaN | NaN | NaN | NaN | 67.468 | 0.748800 | 0.036 | 0.363 | 9.4 | 3.301 | 7.9 | 1834.4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 11.895 | NaN | 22.666667 | 15.333333 | 20.000000 | 26.666667 | 9.333333 | 8.333333 | 4.333333 | 6.333333 | NaN | NaN | NaN | NaN | NaN | 30.000000 | 38.666667 | 13.000000 | 23.666667 | 34.000000 | NaN | NaN | 0.097 | 2.749 | 11.9 | 5.0 | 17.3 | 28.993 | 44.553 | 5.236 | 4.401 | 0.885 | NaN | NaN | NaN | NaN | NaN | NaN | 0.219 | 0.176 | NaN | 6.917 | 16.714 | 0.385 | NaN | NaN | NaN | NaN | NaN | 2.000000 | 12.3 |
| 21 | 2003 | 5.496667 | 8.806667 | 1.313333 | 4.116667 | 2.706667 | 1.646667 | 0.613333 | 33.533333 | NaN | NaN | NaN | 19.066667 | 54.133333 | 4.8 | 3.7 | 1.20 | NaN | NaN | NaN | 0.650 | NaN | 13.222 | NaN | NaN | 0.555 | 1.023 | 0.306 | 0.374 | 0.677 | 0.764 | 67.278 | 0.760600 | 0.023 | 0.342 | 9.1 | 3.621 | 7.7 | 1852.8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.960 | NaN | 22.333333 | 15.666667 | 19.000000 | 25.333333 | 9.666667 | 8.666667 | 4.666667 | 5.666667 | NaN | NaN | NaN | NaN | NaN | 28.000000 | 36.333333 | 13.000000 | 23.333333 | 33.000000 | NaN | NaN | 0.094 | 7.427 | 12.5 | 5.2 | 17.6 | 28.239 | 46.051 | 4.984 | 3.371 | 0.478 | NaN | NaN | NaN | NaN | NaN | NaN | 0.225 | 0.176 | 0.133 | 6.802 | 11.423 | 0.644 | NaN | NaN | NaN | NaN | NaN | 1.000000 | 12.5 |
| 22 | 2004 | 5.540000 | 8.980000 | 1.300000 | 4.170000 | 2.740000 | 1.630000 | 0.600000 | 33.400000 | 55.0 | 41.0 | 20.0 | 19.000000 | 54.600000 | 4.0 | 2.5 | 1.35 | 3.3 | NaN | NaN | 0.469 | 10.8 | 13.162 | NaN | NaN | 0.558 | 1.016 | 0.284 | 0.443 | 0.663 | 0.787 | 67.087 | 0.772400 | 0.033 | 0.372 | 10.3 | 4.029 | 7.9 | 1985.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.082 | NaN | 22.000000 | 16.000000 | 18.000000 | 24.000000 | 10.000000 | 9.000000 | 5.000000 | 5.000000 | NaN | NaN | NaN | NaN | NaN | 26.000000 | 34.000000 | 13.000000 | 23.000000 | 32.000000 | NaN | NaN | 0.092 | 3.252 | 13.1 | 5.2 | 18.1 | 27.533 | 43.883 | 5.143 | 3.051 | 0.541 | NaN | NaN | NaN | NaN | NaN | NaN | 0.244 | 0.203 | 0.172 | 6.188 | 11.499 | 0.402 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 12.7 |
| 23 | 2005 | 5.450000 | 8.663333 | 1.266667 | 4.116667 | 2.680000 | 1.623333 | 0.610000 | 32.966667 | 56.0 | 42.0 | 20.5 | 19.400000 | 56.100000 | 3.8 | 2.1 | 1.50 | 2.8 | 15.652 | NaN | 0.376 | 11.1 | 12.439 | NaN | NaN | 0.561 | 1.015 | 0.270 | 0.501 | 0.654 | 0.804 | 66.898 | 0.784200 | 0.027 | 0.348 | 11.6 | 4.482 | 8.3 | 2123.6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.452 | 0.001 | 21.000000 | 15.333333 | 16.666667 | 22.333333 | 9.666667 | 10.000000 | 6.000000 | 6.000000 | NaN | NaN | NaN | NaN | NaN | 23.666667 | 31.000000 | 12.000000 | 21.666667 | 29.666667 | NaN | NaN | 0.098 | 2.432 | 13.5 | 5.1 | 17.7 | 27.471 | 45.621 | 5.134 | 3.075 | 0.601 | NaN | NaN | NaN | NaN | NaN | NaN | 0.220 | 0.220 | 0.184 | 6.264 | 14.755 | 0.388 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 14.0 |
| 24 | 2006 | 5.360000 | 8.346667 | 1.233333 | 4.063333 | 2.620000 | 1.616667 | 0.620000 | 32.533333 | 57.0 | 43.0 | 21.0 | 19.800000 | 57.600000 | 3.9 | 1.9 | 1.70 | 2.8 | 14.932 | NaN | 0.295 | 10.7 | 14.612 | 17.900000 | NaN | 0.575 | 0.989 | 0.264 | 0.548 | 0.652 | 0.819 | 66.614 | 0.796000 | 0.026 | 0.341 | 12.3 | 4.696 | 8.6 | 2396.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.520 | 0.001 | 20.000000 | 14.666667 | 15.333333 | 20.666667 | 9.333333 | 11.000000 | 7.000000 | 7.000000 | NaN | NaN | NaN | NaN | NaN | 21.333333 | 28.000000 | 11.000000 | 20.333333 | 27.333333 | NaN | NaN | 0.099 | 1.922 | 13.3 | 6.1 | 20.1 | 27.988 | 43.394 | 5.161 | 3.331 | 0.575 | 64.286 | 33.929 | 7.143 | 152.733 | 92.926 | 9.003 | 0.266 | 0.221 | 0.180 | 6.358 | 11.710 | 0.469 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 12.1 |
| 25 | 2007 | 5.270000 | 8.030000 | 1.200000 | 4.010000 | 2.560000 | 1.610000 | 0.630000 | 32.100000 | 56.0 | 42.0 | 20.0 | 20.200000 | 59.100000 | 3.6 | 1.2 | 1.40 | 3.1 | 14.743 | 3.250 | 0.213 | 10.9 | 13.441 | 19.933333 | NaN | 0.576 | 0.987 | 0.267 | 0.632 | 0.649 | 0.846 | 66.312 | 0.812857 | 0.025 | 0.328 | 13.1 | 5.000 | 8.3 | 2440.0 | 10.40 | 89.60 | 6.60 | 12.70 | 36.20 | NaN | NaN | 43.0 | 7.40 | 1.8 | 5.10 | 0.20 | 97.60 | NaN | NaN | NaN | NaN | 67.70 | 11.855 | 0.001 | 19.000000 | 14.000000 | 14.000000 | 19.000000 | 9.000000 | 12.000000 | 8.000000 | 8.000000 | 12.0 | 1.579 | 2.842 | 1.786 | 2.857 | 19.000000 | 25.000000 | 10.000000 | 19.000000 | 25.000000 | 34.0 | 14.0 | 0.114 | 2.870 | 13.1 | 5.4 | 17.2 | 28.624 | 43.098 | 5.204 | 3.488 | 0.601 | 63.421 | 37.691 | 7.441 | 159.615 | 105.128 | 7.692 | 0.275 | 0.226 | 0.188 | 6.183 | 12.075 | 0.577 | NaN | NaN | NaN | NaN | NaN | 0.000000 | 11.9 |
| 26 | 2008 | 5.320000 | 8.350000 | 1.290000 | 3.990000 | 2.560000 | 1.610000 | 0.630000 | 33.000000 | 55.0 | 41.0 | 19.0 | 19.800000 | 59.000000 | 4.0 | 2.1 | 1.10 | 3.2 | 14.154 | 3.389 | 0.174 | 11.1 | 12.534 | 21.966667 | NaN | 0.567 | 1.009 | 0.267 | 0.621 | 0.641 | 0.836 | 66.013 | 0.829714 | 0.034 | 0.300 | 13.8 | 4.708 | 9.1 | 2718.2 | 11.18 | 89.54 | 6.18 | 13.42 | 35.82 | NaN | NaN | 43.3 | 7.66 | 2.1 | 5.18 | 0.22 | 97.64 | NaN | NaN | NaN | NaN | 67.06 | 12.670 | 0.001 | 20.000000 | 13.000000 | 12.000000 | 19.000000 | 8.000000 | 12.000000 | 6.000000 | 6.000000 | 11.0 | 1.250 | 2.800 | 1.538 | 2.846 | 17.000000 | 26.000000 | 10.000000 | 17.000000 | 27.000000 | 42.0 | 11.0 | 0.120 | 8.929 | 12.4 | 5.1 | 16.8 | 28.846 | 43.389 | 5.276 | 3.652 | 0.467 | 67.344 | 32.066 | 4.787 | 151.190 | 92.262 | 3.571 | 0.270 | 0.191 | 0.176 | 6.114 | 11.972 | 0.440 | NaN | NaN | NaN | NaN | NaN | 2.000000 | 12.5 |
| 27 | 2009 | 5.240000 | 8.190000 | 1.270000 | 3.910000 | 2.530000 | 1.580000 | 0.630000 | 32.700000 | 54.5 | 40.0 | 18.5 | 20.000000 | 57.600000 | 5.8 | 2.9 | 1.40 | 4.3 | 13.436 | 5.233 | 0.374 | 14.1 | 12.873 | 24.000000 | NaN | 0.576 | 0.996 | 0.278 | 0.554 | 0.650 | 0.810 | 65.709 | 0.846571 | 0.041 | 0.295 | 10.8 | 4.652 | 9.7 | 2954.5 | 11.96 | 89.48 | 5.76 | 14.14 | 35.44 | NaN | NaN | 43.6 | 7.92 | 2.4 | 5.26 | 0.24 | 97.68 | NaN | NaN | NaN | NaN | 66.42 | 12.335 | 0.002 | 21.000000 | 14.000000 | 13.000000 | 18.000000 | 9.000000 | 9.000000 | 5.000000 | 5.000000 | 7.0 | 1.429 | 2.571 | 1.714 | 2.929 | 17.000000 | 25.000000 | 11.000000 | 20.000000 | 30.000000 | 42.0 | 14.0 | 0.175 | 24.076 | 12.4 | 6.0 | 17.9 | 27.606 | 41.846 | 5.277 | 3.726 | 0.564 | 62.500 | 25.875 | 6.250 | 135.866 | 103.951 | 11.246 | 0.289 | 0.202 | 0.189 | 6.313 | 15.805 | 0.543 | 0.0840 | 1.10 | 20.1000 | 5.70 | 14.20 | 1.000000 | 11.5 |
| 28 | 2010 | 5.290000 | 8.250000 | 1.240000 | 4.000000 | 2.550000 | 1.570000 | 0.620000 | 32.300000 | 54.0 | 39.0 | 18.0 | 20.100000 | 60.100000 | 6.1 | 3.0 | 1.50 | 3.9 | 13.769 | 5.601 | 0.546 | 13.4 | 12.722 | 23.966667 | NaN | 0.556 | 1.031 | 0.287 | 0.573 | 0.669 | 0.824 | 65.405 | 0.863429 | 0.052 | 0.299 | 10.1 | 4.777 | 9.7 | 3006.4 | 12.74 | 89.42 | 5.34 | 14.86 | 35.06 | NaN | NaN | 43.9 | 8.18 | 2.7 | 5.34 | 0.26 | 97.72 | NaN | NaN | NaN | NaN | 65.78 | 12.428 | 0.003 | 21.000000 | 15.000000 | 13.000000 | 18.000000 | 10.000000 | 11.000000 | 5.000000 | 4.000000 | 6.0 | 1.333 | 2.857 | 1.667 | 3.000 | 19.000000 | 26.000000 | 13.000000 | 23.000000 | 32.000000 | 33.0 | 18.0 | 0.197 | 10.734 | 12.1 | 6.0 | 18.3 | 28.846 | 42.217 | 5.462 | 3.920 | 0.617 | 63.789 | 28.075 | 5.590 | 146.951 | 104.268 | 4.573 | 0.291 | 0.207 | 0.200 | 5.986 | 9.654 | 0.548 | 0.0890 | 1.85 | 19.6000 | 4.25 | 11.75 | 1.000000 | 10.8 |
| 29 | 2011 | 5.910000 | 9.860000 | 1.440000 | 4.410000 | 2.670000 | 1.620000 | 0.610000 | 35.000000 | 55.0 | 40.2 | 18.8 | 18.900000 | 57.500000 | 6.0 | 2.9 | 1.70 | 4.0 | 13.285 | 5.592 | 0.530 | 13.2 | 13.693 | 23.933333 | NaN | 0.557 | 1.031 | 0.291 | 0.529 | 0.678 | 0.813 | 65.103 | 0.880286 | 0.057 | 0.254 | 9.0 | 4.703 | 9.6 | 3106.5 | 13.52 | 89.36 | 4.92 | 15.58 | 34.68 | NaN | NaN | 44.2 | 8.44 | 3.0 | 5.42 | 0.28 | 97.76 | NaN | NaN | NaN | NaN | 65.14 | 12.721 | 0.003 | 21.000000 | 15.000000 | 14.000000 | 21.000000 | 10.000000 | 9.000000 | 5.000000 | 5.000000 | 8.0 | 1.524 | 3.000 | 1.800 | 3.000 | 18.000000 | 28.000000 | 11.000000 | 19.000000 | 29.000000 | 34.0 | 13.0 | 0.170 | 7.118 | 11.0 | 5.7 | 16.6 | 31.948 | 48.219 | 5.625 | 4.116 | 0.634 | 60.906 | 25.149 | 4.291 | 146.407 | 98.204 | 9.281 | 0.284 | 0.199 | 0.214 | 5.839 | 8.440 | 0.453 | 0.0940 | 2.60 | 19.1000 | 2.80 | 9.30 | 1.000000 | 10.3 |
| 30 | 2012 | 5.160000 | 7.990000 | 1.220000 | 3.930000 | 2.640000 | 1.680000 | 0.630000 | 32.200000 | 56.0 | 41.4 | 19.6 | 20.100000 | 58.700000 | 6.4 | 3.9 | 1.60 | 4.2 | 13.264 | 5.885 | 0.851 | 13.5 | 13.555 | 23.900000 | NaN | 0.554 | 1.037 | 0.292 | 0.487 | 0.668 | 0.794 | 64.806 | 0.897143 | 0.059 | 0.171 | 8.7 | 4.805 | 9.7 | 3156.8 | 14.30 | 89.30 | 4.50 | 16.30 | 34.30 | 6.7 | 13.6 | 44.5 | 8.70 | 3.3 | 5.50 | 0.30 | 97.80 | 6.6 | 4.5 | 4.7 | 55.5 | 64.50 | 12.402 | 0.003 | 21.000000 | 15.000000 | 14.000000 | 19.000000 | 9.000000 | 9.000000 | 6.000000 | 5.000000 | 8.0 | 1.238 | 2.952 | 1.533 | 3.600 | 20.000000 | 27.000000 | 12.000000 | 23.000000 | 31.000000 | 35.0 | 14.0 | 0.140 | 6.752 | 10.8 | 5.4 | 16.9 | 32.055 | 49.784 | 5.740 | 3.996 | 0.509 | 59.096 | 20.137 | 1.716 | 143.353 | 92.486 | 0.289 | 0.272 | 0.183 | 0.194 | 5.247 | 10.209 | 0.473 | 0.0990 | 2.20 | 21.2805 | 3.75 | 10.50 | 1.000000 | 9.1 |
| 31 | 2013 | 5.430000 | 8.460000 | 1.320000 | 4.150000 | 2.620000 | 1.660000 | 0.630000 | 33.600000 | 57.0 | 42.6 | 20.4 | 19.700000 | 58.300000 | 5.8 | 3.6 | 1.60 | 4.1 | 14.085 | 5.333 | 0.700 | 11.9 | 14.080 | 24.233333 | 1.793 | 0.563 | 1.021 | 0.294 | 0.513 | 0.663 | 0.795 | 64.412 | 0.914000 | 0.117 | 0.182 | 8.5 | 5.038 | 9.4 | 3346.4 | 14.50 | 89.50 | 6.10 | 16.40 | 33.70 | 7.2 | 14.5 | 43.3 | 8.70 | 4.4 | 5.80 | 0.50 | 98.20 | 4.5 | 4.3 | 6.5 | 54.9 | 63.40 | 12.166 | 0.003 | 22.000000 | 15.000000 | 12.000000 | 18.000000 | 10.000000 | 10.000000 | 4.000000 | 2.000000 | 7.0 | 1.318 | 2.773 | 1.667 | 3.133 | 16.000000 | 24.000000 | 13.000000 | 21.000000 | 30.000000 | 39.0 | 12.0 | 0.118 | 3.526 | 10.6 | 5.7 | 17.9 | 32.636 | 51.101 | 5.835 | 3.935 | 0.490 | 59.833 | 29.167 | 3.500 | 162.393 | 94.017 | 9.972 | 0.265 | 0.182 | 0.195 | 5.231 | 10.806 | 0.478 | 0.1040 | 1.80 | 23.4610 | 4.70 | 11.70 | 1.000000 | 11.2 |
| 32 | 2014 | 5.800000 | 9.470000 | 1.370000 | 4.095000 | 2.770000 | 1.680000 | 0.610000 | 34.300000 | 58.0 | 43.8 | 21.2 | 19.000000 | 56.200000 | 5.4 | 2.9 | 1.60 | 4.1 | 14.032 | 4.764 | 0.733 | 11.4 | 13.865 | 24.566667 | 2.047 | 0.543 | 1.060 | 0.296 | 0.541 | 0.650 | 0.801 | 64.000 | NaN | 0.192 | 0.178 | 9.3 | 5.301 | 9.4 | 3453.3 | 15.50 | 91.40 | 6.20 | 18.60 | 33.40 | 7.0 | 14.0 | 41.5 | 8.40 | 4.0 | 5.40 | 0.70 | 98.40 | 3.9 | 3.9 | 5.3 | 53.4 | 62.60 | 11.721 | 0.003 | 21.000000 | 16.000000 | 13.000000 | 17.000000 | 10.000000 | 10.000000 | 6.000000 | 4.000000 | 7.0 | 1.667 | 2.952 | 2.062 | 3.375 | 18.000000 | 25.000000 | 13.000000 | 23.000000 | 31.000000 | 37.0 | 16.0 | 0.100 | 2.784 | 10.2 | 5.2 | 17.3 | 33.513 | 53.722 | 5.959 | 3.703 | 0.475 | 66.667 | 31.111 | 2.778 | 161.433 | 110.744 | 5.785 | 0.239 | 0.164 | 0.191 | 5.179 | 9.536 | 0.595 | 0.1190 | 2.85 | 22.6305 | 4.95 | 11.65 | 1.000000 | 9.9 |
| 33 | 2015 | 5.840000 | 9.690000 | 1.440000 | 4.040000 | 2.620000 | 1.610000 | 0.610000 | 35.000000 | 59.0 | 45.0 | 22.0 | 19.100000 | 57.600000 | 5.4 | 3.1 | 1.50 | 3.9 | 13.614 | 4.683 | 0.707 | 11.3 | 13.891 | 24.900000 | 2.452 | 0.557 | 1.032 | 0.289 | 0.516 | 0.632 | 0.786 | 63.585 | NaN | 0.186 | 0.168 | 8.8 | 5.505 | 9.3 | 3530.1 | 14.60 | 88.90 | 6.20 | 17.40 | 33.10 | 5.8 | 13.7 | 40.5 | 8.60 | 4.0 | 6.10 | 1.10 | 98.00 | 5.2 | 3.3 | 6.1 | 54.8 | 63.10 | 12.263 | 0.003 | 21.000000 | 15.000000 | 11.000000 | 16.000000 | 10.000000 | 11.000000 | 6.000000 | 4.000000 | 6.0 | 1.762 | 2.667 | 2.133 | 2.800 | 14.000000 | 22.000000 | 13.000000 | 21.000000 | 31.000000 | 44.0 | 12.0 | 0.100 | 2.861 | 9.8 | 5.3 | 17.8 | 33.075 | 53.398 | 6.058 | 3.693 | 0.459 | 63.135 | 29.730 | 2.703 | 157.105 | 103.753 | 7.239 | 0.248 | 0.166 | 0.188 | 5.538 | 10.443 | 0.464 | 0.1340 | 3.90 | 21.8000 | 5.20 | 11.60 | 1.000000 | 11.8 |
| 34 | 2016 | 5.520000 | 8.910000 | 1.330000 | 4.160000 | 2.590000 | 1.640000 | 0.630000 | 33.600000 | NaN | NaN | NaN | 19.600000 | 57.300000 | 5.1 | 3.0 | 1.20 | 4.3 | 15.021 | 4.400 | 0.721 | 12.0 | 11.188 | NaN | 2.234 | 0.556 | 1.033 | 0.289 | 0.543 | 0.614 | 0.789 | 63.178 | NaN | 0.191 | 0.119 | 8.0 | 5.797 | NaN | NaN | 15.40 | 87.80 | 6.80 | 18.80 | 32.00 | 6.9 | 14.3 | 40.1 | 10.30 | 4.3 | 5.80 | 0.30 | 97.70 | 3.8 | 4.0 | 4.5 | 48.5 | 63.90 | 12.328 | 0.003 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.108 | 3.776 | 9.4 | 5.2 | 17.8 | 34.409 | 56.221 | 6.200 | 3.600 | 0.400 | 55.321 | 28.432 | 2.828 | 160.976 | 111.653 | 16.531 | 0.282 | 0.179 | 0.201 | 5.674 | 10.654 | 0.341 | 0.1355 | 3.00 | 22.1000 | 5.20 | 13.75 | 1.000000 | 12.0 |
| 35 | 2017 | 5.700000 | 9.460000 | 1.380000 | 4.060000 | 2.620000 | 1.600000 | 0.610000 | 34.300000 | NaN | NaN | NaN | NaN | NaN | 4.7 | 3.1 | 1.20 | 4.4 | NaN | NaN | 0.735 | 11.8 | 12.092 | NaN | 3.041 | NaN | NaN | 0.284 | 0.584 | 0.601 | 0.802 | 62.765 | NaN | 0.282 | 0.133 | 7.9 | 6.105 | NaN | NaN | 16.70 | 88.20 | 7.60 | 19.90 | 31.90 | 6.6 | 14.3 | 38.8 | 9.30 | 4.9 | 5.60 | 0.50 | 98.10 | 3.9 | 2.6 | 3.0 | 49.8 | 62.50 | 12.636 | 0.002 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.096 | 1.048 | 9.0 | NaN | NaN | 32.167 | NaN | 6.200 | 3.700 | 0.400 | 51.050 | 25.000 | 0.000 | 170.000 | 116.216 | 15.946 | 0.308 | 0.192 | 0.212 | 5.507 | 7.301 | 0.517 | 0.1370 | 2.10 | 22.4000 | 5.20 | 15.90 | 1.000000 | 9.4 |
| 36 | 2018 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.3 | 2.6 | 1.60 | 4.3 | NaN | NaN | NaN | 11.9 | NaN | NaN | 2.810 | NaN | NaN | 0.282 | 0.590 | 0.590 | 0.801 | 62.355 | NaN | 0.438 | 0.156 | 7.8 | 6.240 | NaN | NaN | 16.60 | 87.50 | 8.60 | 20.90 | 31.80 | 6.9 | 14.9 | 39.4 | 9.30 | 5.6 | 5.90 | 0.60 | 98.00 | 3.0 | 2.4 | 2.0 | 50.0 | 63.50 | 13.667 | 0.002 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.088 | 2.185 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.315 | 0.179 | 0.216 | 3.906 | NaN | 0.326 | NaN | NaN | NaN | NaN | NaN | NaN | 9.2 |
new_df_imputed
| Unnamed: 0 | Q5 to Q1 income share ratio | D10 to D1 income share ratio | D10 to D1-4 income share ratio (Palma) | P90 to P10 income ratio | P80 to P20 income ratio | P80 to P50 income ratio | P50 to P20 income ratio | GINI | Top 10 percent wealth share | Top 5 percent wealth share | Top 1 percent wealth share | Lower deciles income share | Middle class income share | Unemployment rate | Unemployment rate, 60-64 | Unemployment rate, 65+ | Underemployment rate | Percentage of employees working long hours | Labour market insecurity | Long-term unemployment rate | Percentage of youth NEET | Percentage of workforce on low pay, OECD definition | Percentage of workforce on low pay, relative to minimum wage | Minimum to living wage gap | Labour share of income | Labour productivity to real product wages ratio | Percentage of households spending above 30% of income on rental | Percentage of households spending above 30% of income on housing | House affordability, rent | House affordability, purchase | Percentage of home ownership | Percentage of homelessness in population | Percentage of Priority A state housing applicants in population | Percentage of Priority B state housing applicants in population | Percentage of disposable income spent on household debt | Median multiple for housing | Health expenditure as a percentage of GDP | Health expenditure per capita, PPP | Prevalence of depression, adult | Prevalence of self-rated health as good or better, adult | Prevalence of pyschological distress, adult | Prevalence of mood_anxiety disorders, adult | Prevalence of healthy weight, adult | Prevalence of unmet need for after-hours care due to cost, adult | Prevalence of unmet need for GP due to cost, adult | Prevalence of adequate vegetable and fruit intake, adult | Prevalence of breakfasting at home less than 5 days, child | Prevalence of emotional_behavioural problems, child | Prevalence of diabetes, adult | Prevalence of depression, child | Prevalence of good or better parent-rated health, child | Prevalence of unfulfilled prescriptions due to cost, child | Prevalence of unmet need to after hours care due to cost, child | Prevalence of unmet need for GP due to cost, child | Prevalence of adequate vegetable and fruit intake, child | Prevalence of healthy weight, child | Rate of suicides | Prevalence of problem gambling interventions | Prevalence of poverty 60% ML | Prevalence of poverty 50% ML | Prevalence of poverty 50% AL | Prevalence of poverty 60% AL | Prevalence of poverty 40% AL | Prevalence of poverty, 60% ML, elderly | Prevalence of poverty 50% ML, elderly | Prevalence of poverty 50% AL, elderly | Prevalence of poverty 60% AL, elderly | Poverty risk ratio, 60% AL, single under 65 | Poverty risk ratio, 60% AL, solo parent | Poverty risk ratio, 50% AL, single under 65 | Poverty risk ratio, 50% AL, solo parent | Prevalence of poverty, 50% AL, child | Prevalence of poverty, 60% AL, child | Prevalence of poverty, 40% ML, child | Prevalence of poverty, 50% ML, child | Prevalence of poverty, 60% ML, child | Prevalence of poverty, 60% AL, children with part time working parent_s | Prevalence of poverty, 60% AL, children with full time working parent_s | Prevalence of personal insolvencies | Loan delinquencies | Tertiary education participation | Education expenditure, GDP | Education expenditure, government expenses | Tertiary loan as a percentage of income | Tertiary loan leaving balance as a percentage of income | University fees to income ratio | Polytechnic fees to income ratio | Wānanga fees to income ratio | Degree earnings premium, hourly | Diploma_certificate earnings premium, hourly | School earnings premium, hourly | Degree earnings premium, weekly | Diploma_certificate earnings premium, weekly | School earnings premium, weekly | Percentage of population in remand | Percentage of population sentenced | Percentage of population post-sentence | Incidence of crime victimisation | Rate of murder and homicide | Regional GDP variation | Regional income inadequacy variation | Inadequacy Of Income, Gender | Inadequacy Of Income, Housing Tenure | Inadequacy Of Income, Long-Term Migrant | Inadequacy Of Income, Māori | Low Income, Gender | Gender pay gap | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1982 | 1982.0 | 4.110000 | 6.120000 | 0.910000 | 3.260000 | 2.300000 | 1.510000 | 0.660000 | 27.200000 | 55.7 | 41.60 | 19.80 | 22.200000 | 63.300000 | 5.9 | 2.1 | 2.30 | 3.24 | 14.5834 | 4.6130 | 0.9176 | 11.58 | 13.3956 | 21.546667 | 2.3134 | 0.5744 | 0.9870 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.8644 | 0.813869 | 0.0306 | 0.3224 | 11.04 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 13.107 | 0.0012 | 14.000000 | 9.000000 | 12.000000 | 19.800000 | 6.000000 | 5.000000 | 2.000000 | 4.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 18.000000 | 27.000000 | 8.000000 | 13.000000 | 21.000000 | 37.0 | 14.0 | 0.1094 | 3.9046 | 11.02 | 5.26 | 17.04 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2442 | 0.2092 | 0.1834 | 6.9278 | 13.4208 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.500000 | 13.58 |
| 1983 | 1983.0 | 4.125000 | 6.125000 | 0.920000 | 3.285000 | 2.305000 | 1.525000 | 0.665000 | 27.350000 | 55.7 | 41.60 | 19.80 | 22.250000 | 62.900000 | 5.9 | 2.1 | 2.30 | 3.24 | 14.5834 | 4.6130 | 0.9176 | 11.58 | 13.3956 | 21.546667 | 2.3134 | 0.5758 | 0.9818 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 70.1950 | 0.813869 | 0.0306 | 0.3224 | 11.04 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 13.107 | 0.0012 | 14.000000 | 8.500000 | 12.500000 | 19.800000 | 5.500000 | 4.000000 | 2.000000 | 3.500000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 19.500000 | 27.000000 | 8.000000 | 13.500000 | 21.500000 | 37.0 | 14.0 | 0.1094 | 3.4136 | 10.16 | 5.26 | 17.04 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2442 | 0.2092 | 0.1834 | 7.3718 | 13.9738 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.500000 | 13.58 |
| 1984 | 1984.0 | 4.140000 | 6.130000 | 0.930000 | 3.310000 | 2.310000 | 1.540000 | 0.670000 | 27.500000 | 55.7 | 41.60 | 19.80 | 22.300000 | 62.500000 | 5.9 | 2.1 | 2.30 | 3.24 | 14.5834 | 4.6130 | 0.9176 | 11.58 | 13.3456 | 21.546667 | 2.3134 | 0.5780 | 0.9762 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 70.9734 | 0.813869 | 0.0306 | 0.3224 | 11.04 | 4.7076 | 8.36 | 2256.92 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 13.107 | 0.0012 | 14.000000 | 8.000000 | 13.000000 | 19.800000 | 5.000000 | 3.000000 | 2.000000 | 3.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 21.000000 | 27.000000 | 8.000000 | 14.000000 | 22.000000 | 37.0 | 14.0 | 0.1094 | 2.8074 | 9.38 | 5.24 | 16.82 | 28.6048 | 44.4586 | 5.2670 | 3.7830 | 1.2206 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2442 | 0.2092 | 0.1834 | 7.5686 | 14.2394 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.500000 | 13.58 |
| 1985 | 1985.0 | 4.090000 | 6.085000 | 0.925000 | 3.240000 | 2.265000 | 1.520000 | 0.675000 | 27.250000 | 55.7 | 41.60 | 19.80 | 22.550000 | 63.550000 | 5.9 | 2.1 | 2.30 | 3.24 | 14.5834 | 4.6130 | 0.9176 | 11.58 | 13.3956 | 21.546667 | 2.3134 | 0.5744 | 0.9870 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.2606 | 0.813869 | 0.0306 | 0.3224 | 11.82 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 10.332 | 0.0012 | 13.500000 | 8.000000 | 12.000000 | 19.800000 | 4.500000 | 4.500000 | 2.000000 | 3.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 19.000000 | 27.000000 | 7.000000 | 13.000000 | 21.500000 | 37.0 | 14.0 | 0.1094 | 3.9046 | 11.02 | 5.26 | 17.04 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2442 | 0.2092 | 0.1834 | 6.9278 | 13.4208 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.100000 | 13.34 |
| 1986 | 1986.0 | 4.040000 | 6.040000 | 0.920000 | 3.170000 | 2.220000 | 1.500000 | 0.680000 | 27.000000 | 55.7 | 41.60 | 19.80 | 22.800000 | 64.600000 | 4.2 | 1.3 | 2.00 | 3.24 | 14.5834 | 4.6130 | 0.3310 | 11.58 | 13.3162 | 21.546667 | 2.3134 | 0.5742 | 0.9914 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.2606 | 0.813869 | 0.0306 | 0.3224 | 11.66 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 12.634 | 0.0012 | 13.000000 | 8.000000 | 11.000000 | 19.800000 | 4.000000 | 6.000000 | 2.000000 | 3.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 17.000000 | 27.000000 | 6.000000 | 12.000000 | 21.000000 | 37.0 | 14.0 | 0.1212 | 8.3592 | 11.88 | 5.50 | 17.54 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2640 | 0.2120 | 0.1834 | 6.5646 | 13.0912 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.500000 | 12.84 |
| 1987 | 1987.0 | 4.045000 | 6.070000 | 0.915000 | 3.145000 | 2.220000 | 1.505000 | 0.680000 | 27.050000 | 55.7 | 41.60 | 19.80 | 22.600000 | 64.150000 | 4.2 | 1.3 | 1.70 | 3.24 | 14.5834 | 4.6130 | 0.4460 | 11.58 | 13.3162 | 21.546667 | 2.3134 | 0.5742 | 0.9914 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.2606 | 0.813869 | 0.0306 | 0.3224 | 11.66 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 14.015 | 0.0012 | 13.500000 | 8.000000 | 11.500000 | 19.800000 | 4.500000 | 6.000000 | 2.000000 | 3.500000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 17.000000 | 27.000000 | 6.500000 | 12.000000 | 21.000000 | 37.0 | 14.0 | 0.1212 | 8.3592 | 11.88 | 5.50 | 17.54 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2640 | 0.2120 | 0.1834 | 6.5646 | 13.0912 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.500000 | 12.84 |
| 1988 | 1988.0 | 4.050000 | 6.100000 | 0.910000 | 3.120000 | 2.220000 | 1.510000 | 0.680000 | 27.100000 | 55.7 | 41.60 | 19.80 | 22.400000 | 63.700000 | 5.8 | 2.3 | 2.30 | 3.24 | 14.5834 | 4.6130 | 0.7820 | 11.58 | 13.3162 | 21.546667 | 2.3134 | 0.5742 | 0.9914 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.2606 | 0.813869 | 0.0306 | 0.3224 | 11.66 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 14.591 | 0.0012 | 14.000000 | 8.000000 | 12.000000 | 19.800000 | 5.000000 | 6.000000 | 2.000000 | 4.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 17.000000 | 27.000000 | 7.000000 | 12.000000 | 21.000000 | 37.0 | 14.0 | 0.1212 | 8.3592 | 11.88 | 5.50 | 17.54 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2640 | 0.2120 | 0.1834 | 6.5646 | 13.0912 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.000000 | 12.84 |
| 1989 | 1989.0 | 4.240000 | 6.245000 | 1.005000 | 3.285000 | 2.305000 | 1.560000 | 0.675000 | 28.650000 | 55.7 | 41.60 | 19.80 | 22.100000 | 61.900000 | 7.3 | 2.3 | 3.40 | 3.24 | 14.5834 | 4.6130 | 1.2690 | 11.58 | 13.3162 | 21.546667 | 2.3134 | 0.5742 | 0.9914 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.2606 | 0.813869 | 0.0306 | 0.3224 | 11.66 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 13.963 | 0.0012 | 14.500000 | 8.000000 | 12.500000 | 19.800000 | 4.500000 | 6.000000 | 2.000000 | 3.500000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 18.500000 | 27.000000 | 6.500000 | 12.500000 | 21.500000 | 37.0 | 14.0 | 0.1212 | 8.3592 | 11.88 | 5.50 | 17.54 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2640 | 0.2120 | 0.1834 | 6.5646 | 13.0912 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.000000 | 12.84 |
| 1990 | 1990.0 | 4.430000 | 6.390000 | 1.100000 | 3.450000 | 2.390000 | 1.610000 | 0.670000 | 30.200000 | 55.7 | 41.60 | 19.80 | 21.300000 | 59.500000 | 8.0 | 3.3 | 2.10 | 3.24 | 14.5834 | 4.6130 | 1.7600 | 11.58 | 13.3956 | 21.546667 | 2.3134 | 0.5744 | 0.9870 | 0.2692 | 0.5712 | 0.6492 | 0.8230 | 68.2606 | 0.813869 | 0.0306 | 0.3224 | 11.82 | 4.7076 | 8.80 | 2526.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 13.532 | 0.0012 | 15.000000 | 8.000000 | 13.000000 | 19.800000 | 4.000000 | 6.000000 | 2.000000 | 3.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 20.000000 | 27.000000 | 6.000000 | 13.000000 | 22.000000 | 37.0 | 14.0 | 0.1212 | 3.9046 | 11.02 | 5.26 | 17.04 | 28.1070 | 43.4696 | 5.2104 | 3.4544 | 0.5616 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2640 | 0.2120 | 0.1834 | 6.9278 | 13.4208 | 0.4834 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.000000 | 13.34 |
| 1991 | 1991.0 | 4.765000 | 7.290000 | 1.140000 | 3.645000 | 2.465000 | 1.640000 | 0.665000 | 31.050000 | 55.7 | 41.60 | 20.00 | 20.200000 | 57.600000 | 10.6 | 2.8 | 2.50 | 3.26 | 14.5834 | 4.6130 | 2.5780 | 11.52 | 12.8088 | 21.546667 | 2.3134 | 0.5760 | 0.9788 | 0.2782 | 0.4996 | 0.6590 | 0.8040 | 73.2980 | 0.760600 | 0.0290 | 0.3532 | 8.78 | 3.7274 | 7.72 | 1794.56 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.72 | 14.02 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.80 | 4.00 | 5.42 | 53.42 | 66.420 | 13.559 | 0.0012 | 18.000000 | 10.500000 | 18.000000 | 25.266667 | 6.000000 | 4.500000 | 1.500000 | 4.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 28.500000 | 36.200000 | 8.500000 | 17.000000 | 27.500000 | 37.0 | 14.0 | 0.1002 | 2.8074 | 8.38 | 4.94 | 16.10 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 63.6914 | 30.9420 | 5.9824 | 149.1622 | 98.4942 | 8.1586 | 0.2126 | 0.1818 | 0.1714 | 7.9938 | 14.0008 | 0.4718 | 0.0940 | 1.91 | 20.7083 | 4.24 | 11.49 | 0.500000 | 14.04 |
| 1992 | 1992.0 | 5.100000 | 8.190000 | 1.180000 | 3.840000 | 2.540000 | 1.670000 | 0.660000 | 31.900000 | 55.6 | 41.28 | 19.82 | 20.000000 | 56.100000 | 10.7 | 2.7 | 1.70 | 3.40 | 14.4720 | 4.7434 | 3.4480 | 11.90 | 12.5286 | 21.926667 | 2.3134 | 0.5760 | 0.9788 | 0.2832 | 0.4706 | 0.6628 | 0.7936 | 72.6660 | 0.760600 | 0.0290 | 0.3532 | 8.66 | 3.7274 | 7.72 | 1794.56 | 12.428 | 89.444 | 5.508 | 14.572 | 35.212 | 6.88 | 14.14 | 43.78 | 8.076 | 2.58 | 5.308 | 0.252 | 97.704 | 4.54 | 3.86 | 4.80 | 52.42 | 66.036 | 13.954 | 0.0018 | 21.000000 | 13.000000 | 23.000000 | 25.266667 | 8.000000 | 3.000000 | 1.000000 | 5.000000 | 9.0 | 1.3848 | 2.8902 | 1.6648 | 3.0606 | 37.000000 | 36.200000 | 11.000000 | 21.000000 | 33.000000 | 35.6 | 14.0 | 0.1002 | 6.4740 | 7.96 | 4.96 | 16.00 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 62.2996 | 28.9962 | 5.2362 | 149.8118 | 98.6024 | 6.1676 | 0.2126 | 0.1818 | 0.1726 | 8.1616 | 14.8172 | 0.4718 | 0.1073 | 2.50 | 20.9422 | 4.19 | 11.39 | 1.000000 | 14.16 |
| 1993 | 1993.0 | 5.100000 | 8.115000 | 1.195000 | 3.900000 | 2.545000 | 1.675000 | 0.660000 | 32.050000 | 56.0 | 41.88 | 20.22 | 19.700000 | 54.300000 | 9.8 | 2.5 | 1.70 | 3.48 | 14.7224 | 4.9456 | 3.3090 | 11.62 | 12.5286 | 21.926667 | 2.3134 | 0.5760 | 0.9788 | 0.2832 | 0.4706 | 0.6628 | 0.7936 | 72.0420 | 0.760600 | 0.0290 | 0.3532 | 8.66 | 3.7274 | 7.72 | 1794.56 | 13.272 | 89.096 | 5.632 | 15.648 | 34.448 | 6.88 | 14.14 | 43.14 | 8.604 | 3.02 | 5.432 | 0.268 | 97.716 | 4.54 | 3.86 | 4.80 | 52.42 | 65.404 | 12.396 | 0.0018 | 21.000000 | 13.500000 | 24.500000 | 25.266667 | 8.500000 | 3.000000 | 1.000000 | 5.500000 | 9.0 | 1.3848 | 2.8902 | 1.6648 | 3.0606 | 39.000000 | 36.200000 | 12.000000 | 21.500000 | 34.000000 | 35.6 | 14.0 | 0.1002 | 1.3800 | 8.20 | 4.96 | 16.00 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 60.6060 | 29.0676 | 4.6838 | 152.6168 | 100.0794 | 8.5592 | 0.2126 | 0.1818 | 0.1726 | 8.0590 | 14.6460 | 0.4718 | 0.1073 | 2.50 | 20.9422 | 4.19 | 11.39 | 1.500000 | 14.16 |
| 1994 | 1994.0 | 5.100000 | 8.040000 | 1.210000 | 3.960000 | 2.550000 | 1.680000 | 0.660000 | 32.200000 | 56.0 | 41.88 | 20.22 | 19.900000 | 54.900000 | 8.4 | 3.3 | 2.30 | 3.48 | 14.7224 | 4.9456 | 2.7500 | 11.62 | 12.5286 | 21.926667 | 2.3134 | 0.5712 | 0.9890 | 0.2826 | 0.4818 | 0.6520 | 0.7926 | 71.4220 | 0.760600 | 0.0290 | 0.3532 | 8.66 | 3.7274 | 7.72 | 1794.56 | 14.064 | 88.852 | 6.084 | 16.656 | 33.816 | 6.88 | 14.14 | 42.12 | 8.828 | 3.46 | 5.484 | 0.316 | 97.792 | 4.54 | 3.86 | 4.80 | 52.42 | 64.748 | 14.137 | 0.0018 | 21.000000 | 14.000000 | 26.000000 | 25.266667 | 9.000000 | 3.000000 | 1.000000 | 6.000000 | 8.2 | 1.4682 | 2.9206 | 1.7696 | 3.1664 | 41.000000 | 36.200000 | 13.000000 | 22.000000 | 35.000000 | 34.6 | 15.0 | 0.1002 | -2.5020 | 7.30 | 4.96 | 16.00 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 58.6348 | 29.0378 | 3.8256 | 157.3354 | 103.6818 | 9.8922 | 0.2126 | 0.1818 | 0.1740 | 8.1810 | 17.1200 | 0.4718 | 0.1109 | 2.35 | 20.8961 | 4.24 | 12.24 | 2.000000 | 14.16 |
| 1995 | 1995.0 | 5.155000 | 8.260000 | 1.245000 | 3.895000 | 2.550000 | 1.675000 | 0.655000 | 32.650000 | 56.0 | 41.88 | 20.22 | 19.800000 | 56.100000 | 6.5 | 2.7 | 1.50 | 3.48 | 14.7224 | 4.9456 | 1.6670 | 11.62 | 12.5286 | 21.926667 | 2.3134 | 0.5760 | 0.9788 | 0.2826 | 0.4818 | 0.6520 | 0.7926 | 70.7900 | 0.760600 | 0.0290 | 0.3532 | 8.66 | 3.7274 | 7.72 | 1794.56 | 13.272 | 89.096 | 5.632 | 15.648 | 34.448 | 6.88 | 14.14 | 43.14 | 8.604 | 3.02 | 5.432 | 0.268 | 97.716 | 4.54 | 3.86 | 4.80 | 52.42 | 65.404 | 14.772 | 0.0018 | 20.500000 | 14.000000 | 24.000000 | 25.266667 | 9.000000 | 4.000000 | 2.000000 | 6.000000 | 8.2 | 1.4682 | 2.9206 | 1.7696 | 3.1664 | 37.500000 | 36.200000 | 13.500000 | 22.000000 | 33.500000 | 34.6 | 15.0 | 0.1002 | -0.1840 | 8.10 | 4.96 | 16.00 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 60.6060 | 29.0676 | 4.6838 | 152.6168 | 100.0794 | 8.5592 | 0.2126 | 0.1818 | 0.1740 | 8.3090 | 10.8820 | 0.4718 | 0.1109 | 2.35 | 20.8961 | 4.24 | 12.24 | 2.000000 | 14.16 |
| 1996 | 1996.0 | 5.210000 | 8.480000 | 1.280000 | 3.830000 | 2.550000 | 1.670000 | 0.650000 | 33.100000 | 56.0 | 41.88 | 20.22 | 19.800000 | 55.200000 | 6.3 | 2.9 | 1.40 | 3.24 | 14.7224 | 4.9456 | 1.3330 | 11.40 | 12.5286 | 21.926667 | 2.3134 | 0.5740 | 0.9830 | 0.2782 | 0.4996 | 0.6590 | 0.8040 | 70.2040 | 0.760600 | 0.0290 | 0.3532 | 8.66 | 3.7274 | 7.72 | 1794.56 | 13.272 | 89.096 | 5.632 | 15.648 | 34.448 | 6.88 | 14.14 | 43.14 | 8.604 | 3.02 | 5.432 | 0.268 | 97.716 | 4.54 | 3.86 | 4.80 | 52.42 | 65.404 | 14.462 | 0.0018 | 20.000000 | 14.000000 | 22.000000 | 25.266667 | 9.000000 | 5.000000 | 3.000000 | 6.000000 | 8.2 | 1.4682 | 2.9206 | 1.7696 | 3.1664 | 34.000000 | 36.200000 | 14.000000 | 22.000000 | 32.000000 | 34.6 | 15.0 | 0.1002 | -0.9510 | 8.10 | 4.96 | 16.00 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 60.6060 | 29.0676 | 4.6838 | 152.6168 | 100.0794 | 8.5592 | 0.2126 | 0.1818 | 0.1714 | 8.3340 | 14.7370 | 0.4718 | 0.1073 | 2.50 | 20.9422 | 4.19 | 11.39 | 2.000000 | 14.16 |
| 1997 | 1997.0 | 5.230000 | 8.515000 | 1.280000 | 3.775000 | 2.560000 | 1.655000 | 0.645000 | 33.050000 | 56.0 | 41.88 | 20.22 | 18.900000 | 54.400000 | 6.8 | 3.3 | 1.00 | 3.24 | 14.3752 | 4.7434 | 1.3460 | 11.40 | 13.2700 | 21.926667 | 2.3134 | 0.5770 | 0.9740 | 0.2782 | 0.4996 | 0.6590 | 0.8040 | 69.6330 | 0.760600 | 0.0290 | 0.3532 | 8.66 | 3.7274 | 7.72 | 1794.56 | 12.428 | 89.444 | 5.508 | 14.572 | 35.212 | 6.72 | 14.02 | 43.78 | 8.076 | 2.58 | 5.308 | 0.252 | 97.704 | 4.80 | 4.00 | 5.42 | 53.42 | 66.036 | 14.858 | 0.0018 | 20.000000 | 14.000000 | 20.500000 | 25.266667 | 9.000000 | 6.000000 | 3.500000 | 6.500000 | 9.0 | 1.3848 | 2.8902 | 1.6648 | 3.0606 | 32.000000 | 36.200000 | 14.000000 | 21.500000 | 31.500000 | 35.6 | 14.0 | 0.1002 | 1.8030 | 8.10 | 4.80 | 15.40 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 62.2996 | 28.9962 | 5.2362 | 149.8118 | 98.6024 | 6.1676 | 0.2126 | 0.1818 | 0.1714 | 8.1290 | 17.4530 | 0.4718 | 0.1073 | 2.50 | 20.9422 | 4.19 | 11.39 | 2.000000 | 14.16 |
| 1998 | 1998.0 | 5.250000 | 8.550000 | 1.280000 | 3.720000 | 2.570000 | 1.640000 | 0.640000 | 33.000000 | 56.0 | 41.88 | 20.22 | 19.600000 | 54.900000 | 7.7 | 3.4 | 1.20 | 3.24 | 14.3752 | 4.7434 | 1.5080 | 11.40 | 13.1210 | 21.926667 | 2.3134 | 0.5770 | 0.9760 | 0.2782 | 0.4996 | 0.6590 | 0.8040 | 69.0660 | 0.760600 | 0.0290 | 0.3532 | 8.30 | 3.7274 | 7.72 | 1794.56 | 12.428 | 89.444 | 5.508 | 14.572 | 35.212 | 6.72 | 14.02 | 43.78 | 8.076 | 2.58 | 5.308 | 0.252 | 97.704 | 4.80 | 4.00 | 5.42 | 53.42 | 66.036 | 15.174 | 0.0018 | 20.000000 | 14.000000 | 19.000000 | 25.266667 | 9.000000 | 7.000000 | 4.000000 | 7.000000 | 9.0 | 1.3848 | 2.8902 | 1.6648 | 3.0606 | 30.000000 | 36.200000 | 14.000000 | 21.000000 | 31.000000 | 35.6 | 14.0 | 0.1002 | 3.9990 | 8.20 | 4.90 | 15.70 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 62.2996 | 28.9962 | 5.2362 | 149.8118 | 98.6024 | 6.1676 | 0.2126 | 0.1818 | 0.1714 | 7.8550 | 13.8940 | 0.4718 | 0.1073 | 2.50 | 20.9422 | 4.19 | 11.39 | 2.000000 | 16.20 |
| 1999 | 1999.0 | 5.303333 | 8.520000 | 1.300000 | 3.816667 | 2.593333 | 1.653333 | 0.640000 | 33.266667 | 56.0 | 41.88 | 20.22 | 19.466667 | 54.333333 | 7.0 | 5.4 | 1.80 | 3.24 | 14.3752 | 4.9456 | 1.4850 | 11.40 | 12.2840 | 21.926667 | 2.3134 | 0.5860 | 0.9610 | 0.2832 | 0.4706 | 0.6628 | 0.7936 | 68.4970 | 0.760600 | 0.0290 | 0.3532 | 7.70 | 3.7274 | 7.72 | 1794.56 | 13.272 | 89.096 | 5.632 | 15.648 | 34.448 | 6.72 | 14.02 | 43.14 | 8.604 | 3.02 | 5.432 | 0.268 | 97.716 | 4.80 | 4.00 | 5.42 | 53.42 | 65.404 | 13.473 | 0.0018 | 21.000000 | 14.333333 | 19.666667 | 25.266667 | 9.000000 | 7.333333 | 4.000000 | 7.000000 | 8.2 | 1.4682 | 2.9206 | 1.7696 | 3.1664 | 30.666667 | 36.200000 | 13.666667 | 22.000000 | 32.333333 | 34.6 | 15.0 | 0.1160 | 2.7120 | 9.40 | 5.00 | 15.70 | 29.0686 | 45.4290 | 5.2950 | 4.2304 | 1.5114 | 60.6060 | 29.0676 | 4.6838 | 152.6168 | 100.0794 | 8.5592 | 0.1900 | 0.1880 | 0.1726 | 7.3420 | 13.0380 | 0.4718 | 0.1073 | 2.50 | 20.9422 | 4.19 | 11.39 | 2.333333 | 15.20 |
| 2000 | 2000.0 | 5.356667 | 8.490000 | 1.320000 | 3.913333 | 2.616667 | 1.666667 | 0.640000 | 33.533333 | 55.8 | 41.80 | 20.10 | 19.333333 | 53.766667 | 6.1 | 4.8 | 1.70 | 3.82 | 14.9004 | 4.3746 | 1.2220 | 11.52 | 11.7390 | 21.553333 | 2.3850 | 0.5660 | 1.0000 | 0.2890 | 0.5068 | 0.6290 | 0.7886 | 67.9190 | 0.760600 | 0.1940 | 0.2226 | 9.40 | 4.9294 | 7.50 | 1606.30 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 11.891 | 0.0018 | 22.000000 | 14.666667 | 20.333333 | 25.266667 | 9.000000 | 7.666667 | 4.000000 | 7.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 31.333333 | 36.200000 | 13.333333 | 23.000000 | 33.666667 | 37.0 | 14.0 | 0.0940 | 2.2590 | 10.10 | 5.00 | 16.40 | 30.0950 | 46.7910 | 5.5600 | 5.3690 | 3.8590 | 60.2844 | 31.4236 | 4.4398 | 158.9028 | 103.6370 | 10.5486 | 0.2050 | 0.1830 | 0.1868 | 6.9800 | 14.5180 | 0.4218 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 2.666667 | 14.00 |
| 2001 | 2001.0 | 5.410000 | 8.460000 | 1.340000 | 4.010000 | 2.640000 | 1.680000 | 0.640000 | 33.800000 | 55.8 | 41.80 | 20.10 | 19.200000 | 53.200000 | 5.5 | 3.4 | 1.30 | 3.82 | 14.9004 | 4.3746 | 0.9450 | 11.52 | 12.2290 | 21.553333 | 2.3850 | 0.5530 | 1.0270 | 0.2890 | 0.5068 | 0.6290 | 0.7886 | 67.6570 | 0.737000 | 0.1940 | 0.2226 | 8.50 | 3.2040 | 7.60 | 1693.80 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 13.070 | 0.0018 | 23.000000 | 15.000000 | 21.000000 | 28.000000 | 9.000000 | 8.000000 | 4.000000 | 7.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 32.000000 | 41.000000 | 13.000000 | 24.000000 | 35.000000 | 37.0 | 14.0 | 0.1000 | 3.1100 | 11.00 | 5.10 | 16.80 | 30.4830 | 45.8670 | 5.5520 | 4.9600 | 1.7940 | 60.2844 | 31.4236 | 4.4398 | 158.9028 | 103.6370 | 10.5486 | 0.2240 | 0.1860 | 0.1868 | 6.8410 | 13.6560 | 0.5400 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 3.000000 | 13.10 |
| 2002 | 2002.0 | 5.453333 | 8.633333 | 1.326667 | 4.063333 | 2.673333 | 1.663333 | 0.626667 | 33.666667 | 55.8 | 41.80 | 20.10 | 19.133333 | 53.666667 | 5.3 | 4.0 | 1.70 | 3.82 | 14.9004 | 4.3746 | 0.7820 | 11.52 | 13.6300 | 21.553333 | 2.3850 | 0.5470 | 1.0370 | 0.2890 | 0.5068 | 0.6290 | 0.7886 | 67.4680 | 0.748800 | 0.0360 | 0.3630 | 9.40 | 3.3010 | 7.90 | 1834.40 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 11.895 | 0.0018 | 22.666667 | 15.333333 | 20.000000 | 26.666667 | 9.333333 | 8.333333 | 4.333333 | 6.333333 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 30.000000 | 38.666667 | 13.000000 | 23.666667 | 34.000000 | 37.0 | 14.0 | 0.0970 | 2.7490 | 11.90 | 5.00 | 17.30 | 28.9930 | 44.5530 | 5.2360 | 4.4010 | 0.8850 | 60.2844 | 31.4236 | 4.4398 | 158.9028 | 103.6370 | 10.5486 | 0.2190 | 0.1760 | 0.1868 | 6.9170 | 16.7140 | 0.3850 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 2.000000 | 12.30 |
| 2003 | 2003.0 | 5.496667 | 8.806667 | 1.313333 | 4.116667 | 2.706667 | 1.646667 | 0.613333 | 33.533333 | 55.8 | 41.80 | 20.10 | 19.066667 | 54.133333 | 4.8 | 3.7 | 1.20 | 3.82 | 14.9004 | 4.3746 | 0.6500 | 11.52 | 13.2220 | 21.553333 | 2.3850 | 0.5550 | 1.0230 | 0.3060 | 0.3740 | 0.6770 | 0.7640 | 67.2780 | 0.760600 | 0.0230 | 0.3420 | 9.10 | 3.6210 | 7.70 | 1852.80 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 12.960 | 0.0018 | 22.333333 | 15.666667 | 19.000000 | 25.333333 | 9.666667 | 8.666667 | 4.666667 | 5.666667 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 28.000000 | 36.333333 | 13.000000 | 23.333333 | 33.000000 | 37.0 | 14.0 | 0.0940 | 7.4270 | 12.50 | 5.20 | 17.60 | 28.2390 | 46.0510 | 4.9840 | 3.3710 | 0.4780 | 60.2844 | 31.4236 | 4.4398 | 158.9028 | 103.6370 | 10.5486 | 0.2250 | 0.1760 | 0.1330 | 6.8020 | 11.4230 | 0.6440 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 1.000000 | 12.50 |
| 2004 | 2004.0 | 5.540000 | 8.980000 | 1.300000 | 4.170000 | 2.740000 | 1.630000 | 0.600000 | 33.400000 | 55.0 | 41.00 | 20.00 | 19.000000 | 54.600000 | 4.0 | 2.5 | 1.35 | 3.30 | 14.9004 | 4.3746 | 0.4690 | 10.80 | 13.1620 | 21.553333 | 2.3850 | 0.5580 | 1.0160 | 0.2840 | 0.4430 | 0.6630 | 0.7870 | 67.0870 | 0.772400 | 0.0330 | 0.3720 | 10.30 | 4.0290 | 7.90 | 1985.50 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 12.082 | 0.0018 | 22.000000 | 16.000000 | 18.000000 | 24.000000 | 10.000000 | 9.000000 | 5.000000 | 5.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 26.000000 | 34.000000 | 13.000000 | 23.000000 | 32.000000 | 37.0 | 14.0 | 0.0920 | 3.2520 | 13.10 | 5.20 | 18.10 | 27.5330 | 43.8830 | 5.1430 | 3.0510 | 0.5410 | 60.2844 | 31.4236 | 4.4398 | 158.9028 | 103.6370 | 10.5486 | 0.2440 | 0.2030 | 0.1720 | 6.1880 | 11.4990 | 0.4020 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 0.000000 | 12.70 |
| 2005 | 2005.0 | 5.450000 | 8.663333 | 1.266667 | 4.116667 | 2.680000 | 1.623333 | 0.610000 | 32.966667 | 56.0 | 42.00 | 20.50 | 19.400000 | 56.100000 | 3.8 | 2.1 | 1.50 | 2.80 | 15.6520 | 4.3746 | 0.3760 | 11.10 | 12.4390 | 21.553333 | 2.3850 | 0.5610 | 1.0150 | 0.2700 | 0.5010 | 0.6540 | 0.8040 | 66.8980 | 0.784200 | 0.0270 | 0.3480 | 11.60 | 4.4820 | 8.30 | 2123.60 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 12.452 | 0.0010 | 21.000000 | 15.333333 | 16.666667 | 22.333333 | 9.666667 | 10.000000 | 6.000000 | 6.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 23.666667 | 31.000000 | 12.000000 | 21.666667 | 29.666667 | 37.0 | 14.0 | 0.0980 | 2.4320 | 13.50 | 5.10 | 17.70 | 27.4710 | 45.6210 | 5.1340 | 3.0750 | 0.6010 | 60.2844 | 31.4236 | 4.4398 | 158.9028 | 103.6370 | 10.5486 | 0.2200 | 0.2200 | 0.1840 | 6.2640 | 14.7550 | 0.3880 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 0.000000 | 14.00 |
| 2006 | 2006.0 | 5.360000 | 8.346667 | 1.233333 | 4.063333 | 2.620000 | 1.616667 | 0.620000 | 32.533333 | 57.0 | 43.00 | 21.00 | 19.800000 | 57.600000 | 3.9 | 1.9 | 1.70 | 2.80 | 14.9320 | 4.3746 | 0.2950 | 10.70 | 14.6120 | 17.900000 | 2.3850 | 0.5750 | 0.9890 | 0.2640 | 0.5480 | 0.6520 | 0.8190 | 66.6140 | 0.796000 | 0.0260 | 0.3410 | 12.30 | 4.6960 | 8.60 | 2396.50 | 14.056 | 88.528 | 7.156 | 17.144 | 33.544 | 6.86 | 14.32 | 40.92 | 8.792 | 3.74 | 5.516 | 0.364 | 97.808 | 4.36 | 3.56 | 4.14 | 51.74 | 64.932 | 12.520 | 0.0010 | 20.000000 | 14.666667 | 15.333333 | 20.666667 | 9.333333 | 11.000000 | 7.000000 | 7.000000 | 8.8 | 1.4230 | 2.8140 | 1.7010 | 2.9264 | 21.333333 | 28.000000 | 11.000000 | 20.333333 | 27.333333 | 37.0 | 14.0 | 0.0990 | 1.9220 | 13.30 | 6.10 | 20.10 | 27.9880 | 43.3940 | 5.1610 | 3.3310 | 0.5750 | 64.2860 | 33.9290 | 7.1430 | 152.7330 | 92.9260 | 9.0030 | 0.2660 | 0.2210 | 0.1800 | 6.3580 | 11.7100 | 0.4690 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 0.000000 | 12.10 |
| 2007 | 2007.0 | 5.270000 | 8.030000 | 1.200000 | 4.010000 | 2.560000 | 1.610000 | 0.630000 | 32.100000 | 56.0 | 42.00 | 20.00 | 20.200000 | 59.100000 | 3.6 | 1.2 | 1.40 | 3.10 | 14.7430 | 3.2500 | 0.2130 | 10.90 | 13.4410 | 19.933333 | 2.3850 | 0.5760 | 0.9870 | 0.2670 | 0.6320 | 0.6490 | 0.8460 | 66.3120 | 0.812857 | 0.0250 | 0.3280 | 13.10 | 5.0000 | 8.30 | 2440.00 | 10.400 | 89.600 | 6.600 | 12.700 | 36.200 | 6.86 | 14.32 | 43.00 | 7.400 | 1.80 | 5.100 | 0.200 | 97.600 | 4.36 | 3.56 | 4.14 | 51.74 | 67.700 | 11.855 | 0.0010 | 19.000000 | 14.000000 | 14.000000 | 19.000000 | 9.000000 | 12.000000 | 8.000000 | 8.000000 | 12.0 | 1.5790 | 2.8420 | 1.7860 | 2.8570 | 19.000000 | 25.000000 | 10.000000 | 19.000000 | 25.000000 | 34.0 | 14.0 | 0.1140 | 2.8700 | 13.10 | 5.40 | 17.20 | 28.6240 | 43.0980 | 5.2040 | 3.4880 | 0.6010 | 63.4210 | 37.6910 | 7.4410 | 159.6150 | 105.1280 | 7.6920 | 0.2750 | 0.2260 | 0.1880 | 6.1830 | 12.0750 | 0.5770 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 0.000000 | 11.90 |
| 2008 | 2008.0 | 5.320000 | 8.350000 | 1.290000 | 3.990000 | 2.560000 | 1.610000 | 0.630000 | 33.000000 | 55.0 | 41.00 | 19.00 | 19.800000 | 59.000000 | 4.0 | 2.1 | 1.10 | 3.20 | 14.1540 | 3.3890 | 0.1740 | 11.10 | 12.5340 | 21.966667 | 2.3850 | 0.5670 | 1.0090 | 0.2670 | 0.6210 | 0.6410 | 0.8360 | 66.0130 | 0.829714 | 0.0340 | 0.3000 | 13.80 | 4.7080 | 9.10 | 2718.20 | 11.180 | 89.540 | 6.180 | 13.420 | 35.820 | 6.86 | 14.32 | 43.30 | 7.660 | 2.10 | 5.180 | 0.220 | 97.640 | 4.36 | 3.56 | 4.14 | 51.74 | 67.060 | 12.670 | 0.0010 | 20.000000 | 13.000000 | 12.000000 | 19.000000 | 8.000000 | 12.000000 | 6.000000 | 6.000000 | 11.0 | 1.2500 | 2.8000 | 1.5380 | 2.8460 | 17.000000 | 26.000000 | 10.000000 | 17.000000 | 27.000000 | 42.0 | 11.0 | 0.1200 | 8.9290 | 12.40 | 5.10 | 16.80 | 28.8460 | 43.3890 | 5.2760 | 3.6520 | 0.4670 | 67.3440 | 32.0660 | 4.7870 | 151.1900 | 92.2620 | 3.5710 | 0.2700 | 0.1910 | 0.1760 | 6.1140 | 11.9720 | 0.4400 | 0.1079 | 2.13 | 20.6600 | 4.63 | 12.98 | 2.000000 | 12.50 |
| 2009 | 2009.0 | 5.240000 | 8.190000 | 1.270000 | 3.910000 | 2.530000 | 1.580000 | 0.630000 | 32.700000 | 54.5 | 40.00 | 18.50 | 20.000000 | 57.600000 | 5.8 | 2.9 | 1.40 | 4.30 | 13.4360 | 5.2330 | 0.3740 | 14.10 | 12.8730 | 24.000000 | 2.3850 | 0.5760 | 0.9960 | 0.2780 | 0.5540 | 0.6500 | 0.8100 | 65.7090 | 0.846571 | 0.0410 | 0.2950 | 10.80 | 4.6520 | 9.70 | 2954.50 | 11.960 | 89.480 | 5.760 | 14.140 | 35.440 | 6.86 | 14.32 | 43.60 | 7.920 | 2.40 | 5.260 | 0.240 | 97.680 | 4.36 | 3.56 | 4.14 | 51.74 | 66.420 | 12.335 | 0.0020 | 21.000000 | 14.000000 | 13.000000 | 18.000000 | 9.000000 | 9.000000 | 5.000000 | 5.000000 | 7.0 | 1.4290 | 2.5710 | 1.7140 | 2.9290 | 17.000000 | 25.000000 | 11.000000 | 20.000000 | 30.000000 | 42.0 | 14.0 | 0.1750 | 24.0760 | 12.40 | 6.00 | 17.90 | 27.6060 | 41.8460 | 5.2770 | 3.7260 | 0.5640 | 62.5000 | 25.8750 | 6.2500 | 135.8660 | 103.9510 | 11.2460 | 0.2890 | 0.2020 | 0.1890 | 6.3130 | 15.8050 | 0.5430 | 0.0840 | 1.10 | 20.1000 | 5.70 | 14.20 | 1.000000 | 11.50 |
| 2010 | 2010.0 | 5.290000 | 8.250000 | 1.240000 | 4.000000 | 2.550000 | 1.570000 | 0.620000 | 32.300000 | 54.0 | 39.00 | 18.00 | 20.100000 | 60.100000 | 6.1 | 3.0 | 1.50 | 3.90 | 13.7690 | 5.6010 | 0.5460 | 13.40 | 12.7220 | 23.966667 | 2.3850 | 0.5560 | 1.0310 | 0.2870 | 0.5730 | 0.6690 | 0.8240 | 65.4050 | 0.863429 | 0.0520 | 0.2990 | 10.10 | 4.7770 | 9.70 | 3006.40 | 12.740 | 89.420 | 5.340 | 14.860 | 35.060 | 6.86 | 14.32 | 43.90 | 8.180 | 2.70 | 5.340 | 0.260 | 97.720 | 4.36 | 3.56 | 4.14 | 51.74 | 65.780 | 12.428 | 0.0030 | 21.000000 | 15.000000 | 13.000000 | 18.000000 | 10.000000 | 11.000000 | 5.000000 | 4.000000 | 6.0 | 1.3330 | 2.8570 | 1.6670 | 3.0000 | 19.000000 | 26.000000 | 13.000000 | 23.000000 | 32.000000 | 33.0 | 18.0 | 0.1970 | 10.7340 | 12.10 | 6.00 | 18.30 | 28.8460 | 42.2170 | 5.4620 | 3.9200 | 0.6170 | 63.7890 | 28.0750 | 5.5900 | 146.9510 | 104.2680 | 4.5730 | 0.2910 | 0.2070 | 0.2000 | 5.9860 | 9.6540 | 0.5480 | 0.0890 | 1.85 | 19.6000 | 4.25 | 11.75 | 1.000000 | 10.80 |
| 2011 | 2011.0 | 5.910000 | 9.860000 | 1.440000 | 4.410000 | 2.670000 | 1.620000 | 0.610000 | 35.000000 | 55.0 | 40.20 | 18.80 | 18.900000 | 57.500000 | 6.0 | 2.9 | 1.70 | 4.00 | 13.2850 | 5.5920 | 0.5300 | 13.20 | 13.6930 | 23.933333 | 2.3850 | 0.5570 | 1.0310 | 0.2910 | 0.5290 | 0.6780 | 0.8130 | 65.1030 | 0.880286 | 0.0570 | 0.2540 | 9.00 | 4.7030 | 9.60 | 3106.50 | 13.520 | 89.360 | 4.920 | 15.580 | 34.680 | 6.86 | 14.32 | 44.20 | 8.440 | 3.00 | 5.420 | 0.280 | 97.760 | 4.36 | 3.56 | 4.14 | 51.74 | 65.140 | 12.721 | 0.0030 | 21.000000 | 15.000000 | 14.000000 | 21.000000 | 10.000000 | 9.000000 | 5.000000 | 5.000000 | 8.0 | 1.5240 | 3.0000 | 1.8000 | 3.0000 | 18.000000 | 28.000000 | 11.000000 | 19.000000 | 29.000000 | 34.0 | 13.0 | 0.1700 | 7.1180 | 11.00 | 5.70 | 16.60 | 31.9480 | 48.2190 | 5.6250 | 4.1160 | 0.6340 | 60.9060 | 25.1490 | 4.2910 | 146.4070 | 98.2040 | 9.2810 | 0.2840 | 0.1990 | 0.2140 | 5.8390 | 8.4400 | 0.4530 | 0.0940 | 2.60 | 19.1000 | 2.80 | 9.30 | 1.000000 | 10.30 |
| 2012 | 2012.0 | 5.160000 | 7.990000 | 1.220000 | 3.930000 | 2.640000 | 1.680000 | 0.630000 | 32.200000 | 56.0 | 41.40 | 19.60 | 20.100000 | 58.700000 | 6.4 | 3.9 | 1.60 | 4.20 | 13.2640 | 5.8850 | 0.8510 | 13.50 | 13.5550 | 23.900000 | 2.3850 | 0.5540 | 1.0370 | 0.2920 | 0.4870 | 0.6680 | 0.7940 | 64.8060 | 0.897143 | 0.0590 | 0.1710 | 8.70 | 4.8050 | 9.70 | 3156.80 | 14.300 | 89.300 | 4.500 | 16.300 | 34.300 | 6.70 | 13.60 | 44.50 | 8.700 | 3.30 | 5.500 | 0.300 | 97.800 | 6.60 | 4.50 | 4.70 | 55.50 | 64.500 | 12.402 | 0.0030 | 21.000000 | 15.000000 | 14.000000 | 19.000000 | 9.000000 | 9.000000 | 6.000000 | 5.000000 | 8.0 | 1.2380 | 2.9520 | 1.5330 | 3.6000 | 20.000000 | 27.000000 | 12.000000 | 23.000000 | 31.000000 | 35.0 | 14.0 | 0.1400 | 6.7520 | 10.80 | 5.40 | 16.90 | 32.0550 | 49.7840 | 5.7400 | 3.9960 | 0.5090 | 59.0960 | 20.1370 | 1.7160 | 143.3530 | 92.4860 | 0.2890 | 0.2720 | 0.1830 | 0.1940 | 5.2470 | 10.2090 | 0.4730 | 0.0990 | 2.20 | 21.2805 | 3.75 | 10.50 | 1.000000 | 9.10 |
| 2013 | 2013.0 | 5.430000 | 8.460000 | 1.320000 | 4.150000 | 2.620000 | 1.660000 | 0.630000 | 33.600000 | 57.0 | 42.60 | 20.40 | 19.700000 | 58.300000 | 5.8 | 3.6 | 1.60 | 4.10 | 14.0850 | 5.3330 | 0.7000 | 11.90 | 14.0800 | 24.233333 | 1.7930 | 0.5630 | 1.0210 | 0.2940 | 0.5130 | 0.6630 | 0.7950 | 64.4120 | 0.914000 | 0.1170 | 0.1820 | 8.50 | 5.0380 | 9.40 | 3346.40 | 14.500 | 89.500 | 6.100 | 16.400 | 33.700 | 7.20 | 14.50 | 43.30 | 8.700 | 4.40 | 5.800 | 0.500 | 98.200 | 4.50 | 4.30 | 6.50 | 54.90 | 63.400 | 12.166 | 0.0030 | 22.000000 | 15.000000 | 12.000000 | 18.000000 | 10.000000 | 10.000000 | 4.000000 | 2.000000 | 7.0 | 1.3180 | 2.7730 | 1.6670 | 3.1330 | 16.000000 | 24.000000 | 13.000000 | 21.000000 | 30.000000 | 39.0 | 12.0 | 0.1180 | 3.5260 | 10.60 | 5.70 | 17.90 | 32.6360 | 51.1010 | 5.8350 | 3.9350 | 0.4900 | 59.8330 | 29.1670 | 3.5000 | 162.3930 | 94.0170 | 9.9720 | 0.2650 | 0.1820 | 0.1950 | 5.2310 | 10.8060 | 0.4780 | 0.1040 | 1.80 | 23.4610 | 4.70 | 11.70 | 1.000000 | 11.20 |
| 2014 | 2014.0 | 5.800000 | 9.470000 | 1.370000 | 4.095000 | 2.770000 | 1.680000 | 0.610000 | 34.300000 | 58.0 | 43.80 | 21.20 | 19.000000 | 56.200000 | 5.4 | 2.9 | 1.60 | 4.10 | 14.0320 | 4.7640 | 0.7330 | 11.40 | 13.8650 | 24.566667 | 2.0470 | 0.5430 | 1.0600 | 0.2960 | 0.5410 | 0.6500 | 0.8010 | 64.0000 | 0.880286 | 0.1920 | 0.1780 | 9.30 | 5.3010 | 9.40 | 3453.30 | 15.500 | 91.400 | 6.200 | 18.600 | 33.400 | 7.00 | 14.00 | 41.50 | 8.400 | 4.00 | 5.400 | 0.700 | 98.400 | 3.90 | 3.90 | 5.30 | 53.40 | 62.600 | 11.721 | 0.0030 | 21.000000 | 16.000000 | 13.000000 | 17.000000 | 10.000000 | 10.000000 | 6.000000 | 4.000000 | 7.0 | 1.6670 | 2.9520 | 2.0620 | 3.3750 | 18.000000 | 25.000000 | 13.000000 | 23.000000 | 31.000000 | 37.0 | 16.0 | 0.1000 | 2.7840 | 10.20 | 5.20 | 17.30 | 33.5130 | 53.7220 | 5.9590 | 3.7030 | 0.4750 | 66.6670 | 31.1110 | 2.7780 | 161.4330 | 110.7440 | 5.7850 | 0.2390 | 0.1640 | 0.1910 | 5.1790 | 9.5360 | 0.5950 | 0.1190 | 2.85 | 22.6305 | 4.95 | 11.65 | 1.000000 | 9.90 |
| 2015 | 2015.0 | 5.840000 | 9.690000 | 1.440000 | 4.040000 | 2.620000 | 1.610000 | 0.610000 | 35.000000 | 59.0 | 45.00 | 22.00 | 19.100000 | 57.600000 | 5.4 | 3.1 | 1.50 | 3.90 | 13.6140 | 4.6830 | 0.7070 | 11.30 | 13.8910 | 24.900000 | 2.4520 | 0.5570 | 1.0320 | 0.2890 | 0.5160 | 0.6320 | 0.7860 | 63.5850 | 0.880286 | 0.1860 | 0.1680 | 8.80 | 5.5050 | 9.30 | 3530.10 | 14.600 | 88.900 | 6.200 | 17.400 | 33.100 | 5.80 | 13.70 | 40.50 | 8.600 | 4.00 | 6.100 | 1.100 | 98.000 | 5.20 | 3.30 | 6.10 | 54.80 | 63.100 | 12.263 | 0.0030 | 21.000000 | 15.000000 | 11.000000 | 16.000000 | 10.000000 | 11.000000 | 6.000000 | 4.000000 | 6.0 | 1.7620 | 2.6670 | 2.1330 | 2.8000 | 14.000000 | 22.000000 | 13.000000 | 21.000000 | 31.000000 | 44.0 | 12.0 | 0.1000 | 2.8610 | 9.80 | 5.30 | 17.80 | 33.0750 | 53.3980 | 6.0580 | 3.6930 | 0.4590 | 63.1350 | 29.7300 | 2.7030 | 157.1050 | 103.7530 | 7.2390 | 0.2480 | 0.1660 | 0.1880 | 5.5380 | 10.4430 | 0.4640 | 0.1340 | 3.90 | 21.8000 | 5.20 | 11.60 | 1.000000 | 11.80 |
| 2016 | 2016.0 | 5.520000 | 8.910000 | 1.330000 | 4.160000 | 2.590000 | 1.640000 | 0.630000 | 33.600000 | 57.0 | 42.88 | 20.82 | 19.600000 | 57.300000 | 5.1 | 3.0 | 1.20 | 4.30 | 15.0210 | 4.4000 | 0.7210 | 12.00 | 11.1880 | 23.513333 | 2.2340 | 0.5560 | 1.0330 | 0.2890 | 0.5430 | 0.6140 | 0.7890 | 63.1780 | 0.822297 | 0.1910 | 0.1190 | 8.00 | 5.7970 | 8.82 | 2861.24 | 15.400 | 87.800 | 6.800 | 18.800 | 32.000 | 6.90 | 14.30 | 40.10 | 10.300 | 4.30 | 5.800 | 0.300 | 97.700 | 3.80 | 4.00 | 4.50 | 48.50 | 63.900 | 12.328 | 0.0030 | 21.466667 | 15.400000 | 14.333333 | 19.733333 | 9.866667 | 9.933333 | 5.333333 | 4.333333 | 8.0 | 1.5700 | 2.8468 | 1.8896 | 3.0330 | 19.933333 | 27.666667 | 12.800000 | 22.000000 | 30.933333 | 37.6 | 13.4 | 0.1080 | 3.7760 | 9.40 | 5.20 | 17.80 | 34.4090 | 56.2210 | 6.2000 | 3.6000 | 0.4000 | 55.3210 | 28.4320 | 2.8280 | 160.9760 | 111.6530 | 16.5310 | 0.2820 | 0.1790 | 0.2010 | 5.6740 | 10.6540 | 0.3410 | 0.1355 | 3.00 | 22.1000 | 5.20 | 13.75 | 1.000000 | 12.00 |
| 2017 | 2017.0 | 5.700000 | 9.460000 | 1.380000 | 4.060000 | 2.620000 | 1.600000 | 0.610000 | 34.300000 | 57.0 | 42.88 | 20.82 | 19.213333 | 55.666667 | 4.7 | 3.1 | 1.20 | 4.40 | 14.4808 | 4.4860 | 0.7350 | 11.80 | 12.0920 | 23.513333 | 3.0410 | 0.5546 | 1.0294 | 0.2840 | 0.5840 | 0.6010 | 0.8020 | 62.7650 | 0.760600 | 0.2820 | 0.1330 | 7.90 | 6.1050 | 8.52 | 2589.06 | 16.700 | 88.200 | 7.600 | 19.900 | 31.900 | 6.60 | 14.30 | 38.80 | 9.300 | 4.90 | 5.600 | 0.500 | 98.100 | 3.90 | 2.60 | 3.00 | 49.80 | 62.500 | 12.636 | 0.0020 | 21.466667 | 15.600000 | 15.533333 | 20.933333 | 9.866667 | 9.733333 | 5.533333 | 4.933333 | 8.0 | 1.5700 | 2.8468 | 1.8896 | 3.0330 | 21.933333 | 29.666667 | 12.800000 | 22.400000 | 31.333333 | 37.6 | 13.4 | 0.0960 | 1.0480 | 9.00 | 5.18 | 17.70 | 32.1670 | 49.0996 | 6.2000 | 3.7000 | 0.4000 | 51.0500 | 25.0000 | 0.0000 | 170.0000 | 116.2160 | 15.9460 | 0.3080 | 0.1920 | 0.2120 | 5.5070 | 7.3010 | 0.5170 | 0.1370 | 2.10 | 22.4000 | 5.20 | 15.90 | 1.000000 | 9.40 |
| 2018 | 2018.0 | 5.658000 | 9.198000 | 1.368000 | 4.101000 | 2.644000 | 1.638000 | 0.618000 | 34.160000 | 57.0 | 42.60 | 20.40 | 19.500000 | 57.620000 | 4.3 | 2.6 | 1.60 | 4.30 | 14.0032 | 5.0130 | 0.7192 | 11.90 | 13.0232 | 24.306667 | 2.8100 | 0.5546 | 1.0366 | 0.2820 | 0.5900 | 0.5900 | 0.8010 | 62.3550 | 0.870171 | 0.4380 | 0.1560 | 7.80 | 6.2400 | 9.48 | 3318.62 | 16.600 | 87.500 | 8.600 | 20.900 | 31.800 | 6.90 | 14.90 | 39.40 | 9.300 | 5.60 | 5.900 | 0.600 | 98.000 | 3.00 | 2.40 | 2.00 | 50.00 | 63.500 | 13.667 | 0.0020 | 21.200000 | 15.200000 | 12.800000 | 18.200000 | 9.800000 | 9.800000 | 5.400000 | 4.000000 | 7.2 | 1.5018 | 2.8688 | 1.8390 | 3.1816 | 17.200000 | 25.200000 | 12.400000 | 21.400000 | 30.400000 | 37.8 | 13.4 | 0.0880 | 2.1850 | 9.80 | 5.36 | 17.54 | 33.1600 | 52.8452 | 6.0504 | 3.7262 | 0.4448 | 59.2012 | 28.6880 | 2.3618 | 162.3814 | 107.2766 | 11.0946 | 0.3150 | 0.1790 | 0.2160 | 3.9060 | 9.7480 | 0.3260 | 0.1259 | 2.73 | 22.4783 | 5.05 | 12.92 | 1.000000 | 9.20 |
#This should only be executed if you want to save your work, otherwise the csv is already good just use it
#new_df.to_csv(r'workingdf.csv')
new_df=pd.read_csv(r'workingdf.csv')
I want to try and fill in earlier values for top 10% wealth share and top 1%; I want to check what other wealth and income indicators they correlate with and see if I can get what looks like a reasonable match.
# I want to display in the notebook dynamic charts generated from series here
import matplotlib.pyplot as plt
# create a sample DataFrame
#data = {'x': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]}
#df = pd.DataFrame(data)
# plot the data
plt.plot(new_df[['Top 10 percent wealth share',
'Top 1 percent wealth share',
'Q5 to Q1 income share ratio',
'D10 to D1 income share ratio',
'D10 to D1-4 income share ratio (Palma)',
'P90 to P10 income ratio',
'P80 to P20 income ratio',
'P80 to P50 income ratio',
'P50 to P20 income ratio',
'GINI']])
# add x and y axis labels
plt.xlabel('Year')
plt.ylabel('Top 1% Wealth Share')
# display the plot
plt.show()
This is my intuitive sketch:
Q5 to Q1 income share ratio D10 to D1 income share ratio D10 to D1-4 income share ratio (Palma) P90 to P10 income ratio P80 to P20 income ratio P80 to P50 income ratio P50 to P20 income ratio GINI
# calculate the correlation coefficients between all columns
correlations = new_df.corr()
new_df=new_df.drop(columns='Unnamed: 0')
#correlations
Ok now we're going to try and fill in the gaps.
import pandas as pd
from sklearn.impute import KNNImputer
# create an instance of the KNNImputer class
imputer = KNNImputer(n_neighbors=5)
# fill in missing values using the KNN imputation strategy
new_df_imputed = pd.DataFrame(imputer.fit_transform(new_df), columns=new_df.columns)
new_df_imputed.index=range(1982,2019)
# now we're gonna do it again with the imputer and rate my guesses
# I want to display in the notebook dynamic charts generated from series here
import matplotlib.pyplot as plt
# create a sample DataFrame
#data = {'x': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]}
#df = pd.DataFrame(data)
# plot the data
plt.plot(new_df_imputed[['Top 10 percent wealth share',
'Top 1 percent wealth share',
'Q5 to Q1 income share ratio',
'D10 to D1 income share ratio',
'D10 to D1-4 income share ratio (Palma)',
'P90 to P10 income ratio',
'P80 to P20 income ratio',
'P80 to P50 income ratio',
'P50 to P20 income ratio',
'GINI']])
# add x and y axis labels
plt.xlabel('Year')
plt.ylabel('Top 1% Wealth Share')
# display the plot
plt.show()
Okay well it thinks they were pretty much flat whereas I assumed they used to be flatter.
This seems to me like the machine being much too conservative. It's kind of a status quo bias I think.
But obviously I could be wrong and the opposite could be true
#will try use linear regression machine learning tools
import pandas as pd
from sklearn.linear_model import LinearRegression
# Split your data into input features and target variables
X = new_df_imputed[['Top 10 percent wealth share',
'Top 1 percent wealth share',
'Q5 to Q1 income share ratio',
'D10 to D1 income share ratio',
'D10 to D1-4 income share ratio (Palma)',
'P90 to P10 income ratio',
'P80 to P20 income ratio',
'P80 to P50 income ratio',
'P50 to P20 income ratio',
'GINI']]
y = new_df_imputed['Rate of suicides']
# Create a linear regression model
model = LinearRegression()
# Fit the model to your data
model.fit(X, y)
# Use the model to make predictions
predictions = model.predict(X)
predictions;
array([13.20625065, 13.07744446, 12.94863827, 12.84846187, 12.74828546,
13.19076001, 13.63323455, 13.60803243, 13.58283032, 13.82125593,
14.15935544, 13.81984218, 13.45527804, 14.1060358 , 14.75679356,
14.53694868, 14.34215469, 13.59841697, 12.868969 , 12.15028217,
12.29133115, 12.43238012, 12.44567616, 12.24263164, 12.03958712,
11.89020222, 12.57234912, 12.90319532, 12.17592054, 12.17296657,
12.43306936, 12.2542247 , 11.62416981, 12.79490669, 12.41342634,
13.27401805, 12.99527463])
Ok so might try and check some other data sources; stats nz api, data.govt.nz access; had a brief look but will come back to it at this point. Might see what else is programatically available machine readable etc later, also possibly just find ways of accessing other stuff programatically as well possibly scraping
import requests
url = "https://api.stats.govt.nz/odata/v1/data.json"
headers = {"Ocp-Apim-Subscription-Key": "dc4ec3513435440ea403e1dcfbc65abd"}
response = requests.get(url, headers=headers)
catalogue = response.json()
#print(catalogue);
#not sure about data.govt.nz; want to get this done in the next four hours to limit penalty days to three
#next thing i guess need to come back to the eda and corellations etc; want a heatmap
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Compute the correlation matrix
corr_matrix = new_df_imputed.corr()
# Plot the correlation matrix as a heatmap
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', ax=ax)
# Customize the plot
ax.set_title('Correlation Matrix')
ax.set_xticklabels(corr_matrix.columns, rotation=90)
ax.set_yticklabels(corr_matrix.columns, rotation=0)
It's too big for a heatmap. So what else? Explore correlations? I want to break it into its dimensions and use those.
Wil try factor analysis at various levels next
# Import required libraries
from factor_analyzer import FactorAnalyzer
# Create factor analyzer object
fa = FactorAnalyzer()
# Fit factor analysis on your data
#uncomment next lines once iputed df is done
#fa.fit(new_df_imputed)
# Get factor loadings
#loadings = fa.loadings_ #uncommment this line too
# Print factor loadings
#print(loadings)
I'll turn it into a dataframe
# Create a DataFrame with the loadings
#loadings_df = pd.DataFrame(loadings, index=new_df.columns) #uncomment once imputed df done etc
# Rename the columns to indicate which factor they belong to
#loadings_df.columns = [f"Factor {i+1}" for i in range(3)] #also uncomment this
#loadings_df; should really reproduce this and sort by to make it actually meaningful to look at but time is running low
| Factor 1 | Factor 2 | Factor 3 | |
|---|---|---|---|
| Q5 to Q1 income share ratio | 0.350229 | 0.263251 | 0.744821 |
| D10 to D1 income share ratio | 0.361847 | 0.326036 | 0.704491 |
| D10 to D1-4 income share ratio (Palma) | 0.375079 | 0.298428 | 0.710708 |
| P90 to P10 income ratio | 0.350867 | 0.145675 | 0.752004 |
| P80 to P20 income ratio | 0.444880 | 0.170358 | 0.747626 |
| P80 to P50 income ratio | 0.678365 | 0.220154 | 0.494380 |
| P50 to P20 income ratio | -0.092224 | -0.130321 | -0.772812 |
| GINI | 0.428153 | 0.267289 | 0.705782 |
| Top 10 percent wealth share | -0.091852 | 0.793823 | 0.081337 |
| Top 5 percent wealth share | -0.004880 | 0.719987 | 0.001666 |
| Top 1 percent wealth share | 0.182690 | 0.692383 | -0.021016 |
| Lower deciles income share | -0.551920 | -0.192125 | -0.678646 |
| Middle class income share | -0.795295 | -0.160937 | -0.413007 |
| Unemployment rate | 0.384017 | 0.061980 | -0.124140 |
| Unemployment rate, 60-64 | 0.408594 | 0.313073 | 0.346450 |
| Unemployment rate, 65+ | -0.283550 | -0.122786 | -0.466559 |
| Underemployment rate | -0.409163 | 0.587865 | -0.000205 |
| Percentage of employees working long hours | 0.525627 | -0.140663 | -0.477840 |
| Labour market insecurity | 0.068568 | 0.319287 | 0.370861 |
| Long-term unemployment rate | 0.538077 | 0.126601 | -0.254201 |
| Percentage of youth NEET | -0.328980 | -0.023806 | 0.392515 |
| Percentage of workforce on low pay, OECD definition | -0.146973 | 0.000727 | 0.593454 |
| Percentage of workforce on low pay, relative to minimum wage | -0.259395 | 0.605172 | 0.466440 |
| Minimum to living wage gap | -0.022415 | 0.127476 | -0.417351 |
| Labour share of income | 0.287928 | -0.410215 | -0.406903 |
| Labour productivity to real product wages ratio | -0.528520 | 0.391410 | 0.420790 |
| Percentage of households spending above 30% of income on rental | 0.200092 | 0.543202 | 0.380771 |
| Percentage of households spending above 30% of income on housing | -0.685012 | -0.154001 | -0.337507 |
| House affordability, rent | 0.266817 | -0.374432 | 0.761553 |
| House affordability, purchase | -0.548321 | -0.509726 | -0.108820 |
| Percentage of home ownership | 0.847324 | -0.291290 | -0.095356 |
| Percentage of homelessness in population | -0.890915 | 0.086594 | 0.404026 |
| Percentage of Priority A state housing applicants in population | -0.421164 | 0.665464 | -0.507921 |
| Percentage of Priority B state housing applicants in population | 0.621428 | -0.711094 | 0.199075 |
| Percentage of disposable income spent on household debt | -0.255302 | -0.750508 | 0.034042 |
| Median multiple for housing | -0.823454 | 0.368296 | -0.337473 |
| Health expenditure as a percentage of GDP | -0.930430 | -0.024420 | 0.287866 |
| Health expenditure per capita, PPP | -0.957434 | 0.177926 | 0.329170 |
| Prevalence of depression, adult | 0.243929 | 0.816961 | -0.253500 |
| Prevalence of self-rated health as good or better, adult | -0.140904 | -0.122854 | 0.630237 |
| Prevalence of pyschological distress, adult | 0.042771 | 0.171977 | -0.688606 |
| Prevalence of mood_anxiety disorders, adult | 0.241902 | 0.777756 | -0.381863 |
| Prevalence of healthy weight, adult | -0.230885 | -0.785376 | 0.427694 |
| Prevalence of unmet need for after-hours care due to cost, adult | 0.190909 | -0.308469 | -0.079903 |
| Prevalence of unmet need for GP due to cost, adult | 0.032788 | -0.108336 | -0.348008 |
| Prevalence of adequate vegetable and fruit intake, adult | -0.141069 | -0.502686 | 0.647818 |
| Prevalence of breakfasting at home less than 5 days, child | 0.299486 | 0.629255 | -0.412321 |
| Prevalence of emotional_behavioural problems, child | 0.170516 | 0.798628 | -0.352586 |
| Prevalence of diabetes, adult | -0.009299 | 0.817945 | -0.129552 |
| Prevalence of depression, child | -0.146000 | 0.783074 | 0.115811 |
| Prevalence of good or better parent-rated health, child | 0.024716 | 0.743543 | 0.117121 |
| Prevalence of unfulfilled prescriptions due to cost, child | -0.112003 | -0.194199 | 0.371407 |
| Prevalence of unmet need to after hours care due to cost, child | -0.020207 | -0.236186 | 0.366494 |
| Prevalence of unmet need for GP due to cost, child | -0.229059 | 0.058914 | 0.408177 |
| Prevalence of adequate vegetable and fruit intake, child | -0.250325 | -0.011436 | 0.486584 |
| Prevalence of healthy weight, child | -0.157078 | -0.889561 | 0.056051 |
| Rate of suicides | 0.328255 | 0.038988 | -0.359086 |
| Prevalence of problem gambling interventions | -0.364745 | 0.620731 | 0.493571 |
| Prevalence of poverty 60% ML | 0.555560 | 0.133291 | 0.705871 |
| Prevalence of poverty 50% ML | 0.456947 | 0.153769 | 0.765167 |
| Prevalence of poverty 50% AL | 0.952935 | 0.034855 | -0.047236 |
| Prevalence of poverty 60% AL | 0.934984 | -0.011427 | -0.197305 |
| Prevalence of poverty 40% AL | 0.415516 | 0.145715 | 0.782439 |
| Prevalence of poverty, 60% ML, elderly | -0.264164 | -0.051844 | 0.657728 |
| Prevalence of poverty 50% ML, elderly | -0.128698 | -0.080848 | 0.691796 |
| Prevalence of poverty 50% AL, elderly | 0.746581 | -0.252005 | 0.211777 |
| Prevalence of poverty 60% AL, elderly | 0.144439 | -0.619743 | -0.376211 |
| Poverty risk ratio, 60% AL, single under 65 | -0.074397 | 0.573831 | 0.031137 |
| Poverty risk ratio, 60% AL, solo parent | 0.347192 | 0.185125 | 0.036720 |
| Poverty risk ratio, 50% AL, single under 65 | -0.125510 | 0.722964 | 0.122863 |
| Poverty risk ratio, 50% AL, solo parent | 0.088397 | 0.386369 | 0.150951 |
| Prevalence of poverty, 50% AL, child | 0.942782 | 0.027393 | -0.147385 |
| Prevalence of poverty, 60% AL, child | 0.930817 | 0.045034 | -0.139779 |
| Prevalence of poverty, 40% ML, child | 0.598863 | 0.249849 | 0.609329 |
| Prevalence of poverty, 50% ML, child | 0.597899 | 0.185112 | 0.655111 |
| Prevalence of poverty, 60% ML, child | 0.692520 | 0.222175 | 0.539524 |
| Prevalence of poverty, 60% AL, children with part time working parent_s | -0.492050 | 0.133673 | -0.025018 |
| Prevalence of poverty, 60% AL, children with full time working parent_s | 0.418129 | -0.015209 | 0.247194 |
| Prevalence of personal insolvencies | -0.336167 | -0.341152 | 0.504042 |
| Loan delinquencies | -0.348573 | -0.431667 | 0.326925 |
| Tertiary education participation | -0.404402 | -0.547589 | 0.201886 |
| Education expenditure, GDP | -0.731266 | -0.260656 | 0.137075 |
| Education expenditure, government expenses | -0.667659 | -0.077599 | -0.044736 |
| Tertiary loan as a percentage of income | -0.386253 | 0.847696 | 0.023393 |
| Tertiary loan leaving balance as a percentage of income | -0.200771 | 0.877368 | 0.131334 |
| University fees to income ratio | -0.470856 | 0.826496 | -0.141708 |
| Polytechnic fees to income ratio | 0.535876 | 0.117882 | 0.069422 |
| Wānanga fees to income ratio | 0.701753 | -0.029791 | -0.053507 |
| Degree earnings premium, hourly | -0.236607 | -0.381692 | 0.452254 |
| Diploma_certificate earnings premium, hourly | -0.018438 | -0.329618 | -0.142432 |
| School earnings premium, hourly | -0.073784 | -0.787076 | 0.096951 |
| Degree earnings premium, weekly | 0.168019 | 0.501267 | -0.362791 |
| Diploma_certificate earnings premium, weekly | -0.065215 | 0.404437 | -0.469647 |
| School earnings premium, weekly | 0.100013 | 0.220807 | -0.608263 |
| Percentage of population in remand | -0.855810 | 0.017514 | -0.202576 |
| Percentage of population sentenced | -0.089379 | -0.707511 | -0.071888 |
| Percentage of population post-sentence | -0.643938 | 0.312288 | -0.171581 |
| Incidence of crime victimisation | 0.917069 | -0.222778 | -0.023838 |
| Rate of murder and homicide | 0.689974 | -0.333715 | -0.139775 |
| Regional GDP variation | 0.055365 | -0.179654 | 0.383025 |
| Regional income inadequacy variation | 0.046555 | 0.742580 | -0.399890 |
| Inadequacy Of Income, Gender | 0.002504 | 0.672608 | 0.035075 |
| Inadequacy Of Income, Housing Tenure | -0.195895 | 0.728400 | -0.237233 |
| Inadequacy Of Income, Long-Term Migrant | -0.223058 | 0.220265 | -0.239261 |
| Inadequacy Of Income, Māori | 0.041560 | -0.042832 | -0.502046 |
| Low Income, Gender | 0.604142 | 0.174471 | 0.209359 |
| Gender pay gap | 0.871900 | -0.228892 | -0.091007 |
#scree plot
import matplotlib.pyplot as plt
from factor_analyzer import FactorAnalyzer
# Create factor analyzer object with 3 factors
fa = FactorAnalyzer(n_factors=3)
# Fit factor analysis to data
fa.fit(new_df_imputed)
# Create scree plot
plt.plot(range(1,new_df_imputed.shape[1]+1), fa.get_eigenvalues()[0])
plt.title('Scree Plot')
plt.xlabel('Factors')
plt.ylabel('Eigenvalue')
plt.grid()
plt.show()
Lends itself strongly to a single or very low factor analysis. It seems like there are one or two factors; we could say it's inequality, statistically just a high concentraiton of wealth and an increase over time etc
#will try a pairs plot broken up into chunks
import os
import matplotlib.pyplot as plt
#import seaborn as sns
# create folder if it doesn't exist
#folder = "pairsplots"
#if not os.path.exists(folder):
# os.makedirs(folder)
# create a list of column names to include in the pairs plot
#cols = list(new_df_imputed.columns)
# divide the list of columns into chunks of 10
#n_cols = 10
#chunks = [cols[i:i + n_cols] for i in range(0, len(cols), n_cols)]
# create subplots for each chunk of columns
#for i in range(len(chunks)-1):
# sns.pairplot(new_df_imputed[chunks[i]])
# save the plot to a file
# filename = os.path.join(folder,"chunk_{}.png".format(i))
# plt.savefig(filename)
from IPython.core.display import display, HTML
Principal Component Analysis:
from sklearn.decomposition import PCA
# fit PCA to the data
pca = PCA()
pca.fit(new_df_imputed)
# get the principal components
pcs = pca.transform(new_df_imputed)
# plot the explained variance ratio of each component
plt.plot(pca.explained_variance_ratio_)
plt.xlabel('Component')
plt.ylabel('Explained Variance Ratio')
plt.show()
# will use first two factors for a transform:
# fit PCA to the data
pca = PCA(n_components=2)
pcs = pca.fit_transform(new_df_imputed)
# plot the first two principal components
plt.scatter(pcs[:,0], pcs[:,1])
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.show()
#commented out for avoiding dump
#import pandas as pd
#from sklearn.decomposition import PCA
# fit PCA to the data
#pca = PCA(n_components=2)
#pca.fit(new_df_imputed)
# extract the component weights
#weights = pca.components_
# print the weights and corresponding variable names
#for i, comp in enumerate(weights):
# print(f"Principal Component {i+1} weights:")
# for j, var in enumerate(new_df_imputed.columns):
# print(f"{var}: {comp[j]}")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# fit PCA to the data
pca = PCA(n_components=2)
pca.fit(new_df_imputed)
# extract the component weights
weights = pca.components_
# create a dataframe of the weights with variable names as row labels
weights_df = pd.DataFrame(weights, columns=new_df_imputed.columns)
weights_df.index = ['Component 1', 'Component 2']
# create a bar chart of the weights
fig, ax = plt.subplots(figsize=(10, 5))
weights_df.plot(kind='bar', ax=ax)
ax.set_title('Principal Component Weights')
ax.set_ylabel('Weight')
ax.set_xlabel('Component')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# fit PCA to the data
pca = PCA(n_components=2)
pca.fit(new_df_imputed)
# extract the component weights for the second component
weights = pca.components_[1]
# create a dataframe of the weights with variable names as row labels
weights_df = pd.DataFrame(weights, index=new_df_imputed.columns, columns=['Weight'])
# sort the weights by absolute value
weights_df['AbsWeight'] = weights_df['Weight'].abs()
weights_df.sort_values('AbsWeight', ascending=False, inplace=True)
# create a bar chart of the weights
fig, ax = plt.subplots(figsize=(10, 5))
weights_df['Weight'].plot(kind='bar', ax=ax)
ax.set_title('Second Principal Component Weights')
ax.set_ylabel('Weight')
ax.set_xlabel('Variable')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# fit PCA to the data
pca = PCA(n_components=2)
pca.fit(new_df_imputed)
# extract the component weights for the second component
weights = pca.components_[1]
# create a dataframe of the weights with variable names as row labels
weights_df = pd.DataFrame(weights, index=new_df_imputed.columns, columns=['Weight'])
# sort the weights by absolute value
weights_df['AbsWeight'] = weights_df['Weight'].abs()
weights_df.sort_values('AbsWeight', ascending=False, inplace=True)
# create separate bar charts for positive and negative weights
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))
weights_df[weights_df['Weight'] >= 0]['Weight'].plot(kind='bar', ax=ax1, color='green')
ax1.set_title('Positive Weights')
ax1.set_ylabel('Weight')
ax1.set_xlabel('Variable')
weights_df[weights_df['Weight'] < 0]['Weight'].plot(kind='bar', ax=ax2, color='red')
ax2.set_title('Negative Weights')
ax2.set_ylabel('Weight')
ax2.set_xlabel('Variable')
plt.show()
# extract the component weights for the second component
weights = pca.components_[1]
# create a list of weights and their corresponding variable names
weight_pairs = list(zip(new_df_imputed.columns, weights))
# sort the list by absolute value of the weight
weight_pairs.sort(key=lambda x: abs(x[1]), reverse=True)
# extract the first 5 positive and negative weights
positive_weights = [pair for pair in weight_pairs if pair[1] > 0][:5]
negative_weights = [pair for pair in weight_pairs if pair[1] < 0][:5]
print("First 5 positive weights:")
for pair in positive_weights:
print(pair[0], pair[1])
print("\nFirst 5 negative weights:")
for pair in negative_weights:
print(pair[0], pair[1])
First 5 positive weights: Prevalence of poverty, 60% ML, child 0.4248203862823615 Prevalence of poverty, 50% ML, child 0.37560118529435643 Prevalence of poverty 60% ML 0.2998481999315203 Prevalence of poverty 50% ML 0.2724697600228968 Prevalence of poverty, 40% ML, child 0.23450214487466195 First 5 negative weights: Middle class income share -0.2580506435642271 Diploma_certificate earnings premium, weekly -0.20536839493145578 School earnings premium, weekly -0.14903337865404936 Lower deciles income share -0.1134454701402376 Degree earnings premium, weekly -0.11184118960807596
import matplotlib.pyplot as plt
# extract the component weights for the second component
weights = pca.components_[1]
# create a list of weights and their corresponding variable names
weight_pairs = list(zip(new_df_imputed.columns, weights))
# sort the list by absolute value of the weight
weight_pairs.sort(key=lambda x: abs(x[1]), reverse=True)
# extract the first half of positive weights
positive_weights = [pair for pair in weight_pairs if pair[1] > 0][:len(weight_pairs)//2]
# extract variable names and weights for the bar chart
variables = [pair[0] for pair in positive_weights]
weights = [pair[1] for pair in positive_weights]
# create horizontal bar chart
fig, ax = plt.subplots()
ax.barh(variables, weights)
ax.set_xlabel('Weight')
ax.set_ylabel('Variable Name')
ax.set_title('Positive Weights for Second Principal Component (First Half)')
plt.show()
import matplotlib.pyplot as plt
# extract the component weights for the second component
weights = pca.components_[1]
# create a list of weights and their corresponding variable names
weight_pairs = list(zip(new_df_imputed.columns, weights))
# sort the list by absolute value of the weight
weight_pairs.sort(key=lambda x: abs(x[1]), reverse=True)
# extract the first half of negative weights
negative_weights = [pair for pair in weight_pairs if pair[1] < 0][:len(weight_pairs)//2]
# extract variable names and weights for the bar chart
variables = [pair[0] for pair in negative_weights]
weights = [pair[1] for pair in negative_weights]
# create horizontal bar chart
fig, ax = plt.subplots()
ax.barh(variables, weights)
ax.set_xlabel('Weight')
ax.set_ylabel('Variable Name')
ax.set_title('Negative Weights for Second Principal Component (First Half)')
plt.show()
import matplotlib.pyplot as plt
# extract the component weights for the second component
weights = pca.components_[1]
# create a list of weights and their corresponding variable names
weight_pairs = list(zip(new_df_imputed.columns, weights))
# sort the list by absolute value of the weight
weight_pairs.sort(key=lambda x: abs(x[1]), reverse=True)
# extract the first twenty negative weights
negative_weights = [pair for pair in weight_pairs if pair[1] < 0][:20]
# extract variable names and weights for the bar chart
variables = [pair[0] for pair in negative_weights]
weights = [pair[1] for pair in negative_weights]
# create horizontal bar chart
fig, ax = plt.subplots()
ax.barh(variables, weights)
ax.set_xlabel('Weight')
ax.set_ylabel('Variable Name')
ax.set_title('Negative Weights for Second Principal Component (First Twenty)')
plt.show()
# extract the component weights for the first component
weights = pca.components_[0]
# create a list of weights and their corresponding variable names
weight_pairs = list(zip(new_df_imputed.columns, weights))
# sort the list by weight (in descending order)
weight_pairs.sort(key=lambda x: x[1], reverse=True)
# extract the variable name with the highest positive weight
most_positive_var = weight_pairs[0][0]
print(f"The variable with the highest positive weight for the first principal component is {most_positive_var}.")
The variable with the highest positive weight for the first principal component is Health expenditure per capita, PPP.
import matplotlib.pyplot as plt
# extract the component weights for the first component
weights = pca.components_[0]
# create a list of weights and their corresponding variable names
weight_pairs = list(zip(new_df_imputed.columns, weights))
# sort the list by weight (in descending order)
weight_pairs.sort(key=lambda x: x[1], reverse=True)
# extract the names and weights of the top five variables with highest positive weights
top_positives = weight_pairs[:5][::-1] # [::-1] reverses the order of the list for plotting
# create a bar chart of the top five variables
plt.barh(range(len(top_positives)), [pair[1] for pair in top_positives], align='center')
plt.yticks(range(len(top_positives)), [pair[0] for pair in top_positives])
plt.xlabel("Weight")
plt.title("Top Five Variables with Highest Positive Weights for Component 1")
plt.show()
import matplotlib.pyplot as plt
# extract the second principal component from the PCA object
second_component = pca.transform(new_df_imputed)[:, 1]
# plot the second component against the index of the dataframe
plt.plot(new_df_imputed.index, second_component)
plt.xlabel("Time")
plt.ylabel("Component 2 Score")
plt.title("Second Principal Component over Time")
plt.show()
import matplotlib.pyplot as plt
# extract the first principal component from the PCA object
first_component = pca.transform(new_df_imputed)[:, 0]
# extract the variable that has the highest weight in the first component
variable_name = pca.components_[0].argmax()
my_var = new_df_imputed.iloc[:, variable_name]
# plot the variable against the index of the dataframe
plt.plot(new_df_imputed.index, my_var)
plt.xlabel("Time")
plt.ylabel("Variable Value")
plt.title("Helth expenditure over time")
plt.show()
print("Explained Variance Ratio:")
print(pca.explained_variance_ratio_)
Explained Variance Ratio: [9.99215532e-01 2.75163168e-04]
The first thing I did in assessing the dataseries was to start checking them against the plots on the sharedprosperity website. This was taking a while so I started trying to do it programatically, and then things escalated.
It probably would have been faster and easier to just clean the data in place using a variety of techniques but instead I decided to replace it wholesale with data that wasn't mangled.
To do this I went to each of the "see all indicators" pages on the website and used a chrome extension to batch download the images.
I then selected the content of the table of indicator plots and copied it into a text document, which didn't preserve the images but kept the titles as a list. This is because I noticed the urls of the images, when you removed the .jpg suffix, went to an interactive plotly page where you could retrieve the data.
The first time I tried constructing an index of indicator names to codes I did it manually, typing names into a spreadsheet and then right clicking on images and copying addresses. When I created the completed dataframe from this it had obvious errors so after using one of my extension days I had to go back to square one and do things more programatically.
I saved the images into subfolders named after the category and ordered them by their creation date, then created lists of the filenames to get the urls for the dataseries on plotly. They were downloaded in order from each page so the ordering matched the order of the list of titles.
I took the three name versions for each indicator in the data documentation and created a single csv with all of these, correcting one frameshift error that was in the documentation. I also noticed that the graph titles were similar to the "Indicator name (alternate)" column and used fuzzy logic to match these.
I used this to rename columns. I also created a csv which had the four digit codes for the urls next to the inidicator names from the graph titles and set up a plotly account so I could use the API to get the dataseries and create my own dataframe.
#Ok so we're pretty much starting from scratch. First thing is to bring in the dataset. need pandas
import pandas as pd
df = pd.read_csv("Assignment 1/final_dataset/final_dataset/shared_prosperity_assignment_dataset_mangled.csv")
df=df.sort_values(by='year')
#ok so we've got the dataset imported. Next we want the actual data that's not, you know, mangled.
#To do that we scrape the plotly. We need the list of indicators. We'll get the list of different names
#and maybe rename the column names in df
colmap = pd.read_csv('Default Dataset - Sheet4 (1).csv')
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
This one that you see here is for generating the index of images/indicator codes. It doesn't work perfectly and leaves some of the traces wrong and we had to fix some manually but it's pretty good i mean it's better than my first attempt that was pretty much entirely manual
import os
from pathlib import Path
# Specify the path to the parent directory containing the subfolders
parent_dir = Path(r'C:\Users\02065797\Downloads\scrapedimgs')
# Create a list of all subdirectories in the parent directory
subfolders = [subfolder for subfolder in parent_dir.iterdir() if subfolder.is_dir()]
# Iterate over each subfolder
for subfolder in subfolders:
print(os.path.basename(subfolder))
# Get a list of all files in the subfolder, sorted by creation time
files = sorted(os.listdir(subfolder), key=lambda f: os.stat(os.path.join(subfolder, f)).st_ctime)
# Strip the .jpg suffix from each filename
filenames = [os.path.splitext(file)[0] for file in files]
# Print the list of filenames for the current subfolder
print(filenames)
education ['993', '1065', '1067', '995', '997', '1069', '1071', '1073', '1075', '1078', '1080', '1082', '1084', '1086'] employment ['1033', '1164', '1166', '1035', '1037', '1039', '1041', '1043', '1168', '1170', '1045', '1047', '1049'] general ['1000', '1088', '1090', '1092', '1094', '1096', '1098', '1100'] health ['1003', '1005', '1102', '1104', '1106', '1109', '1111', '1113', '1115', '1117', '1119', '1122', '1124', '1126', '1128', '1130', '1132', '1134', '1136', '1138', '1007', '1009'] housing ['1146', '1016', '1140', '1270', '1148', '1012'] incomewealth ['1018', '1020', '1022', '1024', '1026', '1028', '1030', '1152', '1154', '1156', '1158', '1160', '1162'] safety ['1057', '1059', '1061', '1063', '1214'] socioec ['1172', '1174', '1176', '1180', '1184', '1200', '1052', '1178', '1188', '1190', '1186', '1182', '1209', '1194', '1206', '1202', '1204', '1212', '1198', '1192', '1196', '1054']
colnames=['Indicator','Code']
scrapedcodes=pd.read_csv('indicator codes - sheet2 (3).csv',header=None,names=colnames)
scrapedcodes;
#I took the output from the filename printouts and copied and pasted them into a google doc
#I used gogole docs to strip quotes and commas and square brackets then pasted
#into a google sheet next to the indicator names, then used the split into columns function and
#transposedthe series to line the codes up next to the indicators.
Okay so the following section is for scraping the values from plotly; we use the index, we construct a url using the code, we put our credentials in and ask plotly for the trace which gives the first one which is the interpolated it looks like, that's probably fine in terms of how much we've improved the data compared to how much coding we have to do
#okay so that looks like the best attempt yet, the number of columns matches up, hopefully the mroe programmatic
#approach has helped sort it out. next wee need to set up plotly and scrape the data from tsusnjak so
!pip install chart_studio
Collecting chart_studio Using cached chart_studio-1.1.0-py3-none-any.whl (64 kB) Requirement already satisfied: requests in c:\programdata\anaconda3\lib\site-packages (from chart_studio) (2.28.1) Requirement already satisfied: six in c:\programdata\anaconda3\lib\site-packages (from chart_studio) (1.16.0) Requirement already satisfied: plotly in c:\programdata\anaconda3\lib\site-packages (from chart_studio) (5.9.0) Collecting retrying>=1.3.3 Using cached retrying-1.3.4-py3-none-any.whl (11 kB) Requirement already satisfied: tenacity>=6.2.0 in c:\programdata\anaconda3\lib\site-packages (from plotly->chart_studio) (8.0.1) Requirement already satisfied: certifi>=2017.4.17 in c:\programdata\anaconda3\lib\site-packages (from requests->chart_studio) (2022.9.14) Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\programdata\anaconda3\lib\site-packages (from requests->chart_studio) (1.26.11) Requirement already satisfied: idna<4,>=2.5 in c:\programdata\anaconda3\lib\site-packages (from requests->chart_studio) (3.3) Requirement already satisfied: charset-normalizer<3,>=2 in c:\programdata\anaconda3\lib\site-packages (from requests->chart_studio) (2.0.4) Installing collected packages: retrying, chart_studio Successfully installed chart_studio-1.1.0 retrying-1.3.4
import chart_studio.plotly as py
import chart_studio.tools as tls
tls.set_credentials_file(username='tobiasnash', api_key='YXhqKgaipJ77lcF0iZdn')
The next cell scrapes the ~tsusnjak plotly account for the y values of plots with filenames matching the codes in our indicator code index. This shoud not be executed except for testing as we have saved the resulting dataframe to a csv and now just read_csv to get it.
import requests
#create an empty dictionary to hold the data for each indicator
data_dict = {}
#loop over the dataframe
for index, row in scrapedcodes.iterrows():
#construct the url for the code
code = row['Code']
url = f"https://plotly.com/~tsusnjak/{code}"
#retrieve the json data
figure = py.get_figure(url)
data = figure['data']
# extract the y-values from the json data
y_values = data[0]['y']
# store the y-values in the dictionary for the corresponding indicator
indicator = row['Indicator']
data_dict[indicator] = y_values
#create a new dataframe using the dictionary
new_df = pd.DataFrame(data_dict)
new_df.to_csv(r'programaticscrapeddata.csv') #for safekeeping, can read_csv next time instead of scraping every runtime
Need this if you haven't done a save yet like this is your first run through and you're going to try and do it yourself
new_df=pd.read_csv('programaticscrapeddata.csv') #don't wanna keep starting from scratch
new_df.index=range(1982,2019) #change index to year
df=df.drop(columns=['year']) #get rid of year column and then:
df.index=range(1982,2019) #changing index to year
df; #just checking but supress, leave it otherwise slow af
#need to rename columns in og df
#check colmap
colmap.columns
Index(['Column name in dataset', 'Indicator name',
'Indicator name (alternative)'],
dtype='object')
oldnames=colmap['Column name in dataset']
newnames=colmap['Indicator name (alternative)']
df.rename(columns=dict(zip(oldnames,newnames)), inplace=True)
df;
len(df.columns)
102
colstodrop=set(new_df.columns)-set(df.columns)
colstodrop; #looks like we have case differences, will fuzzywuzzy new_df
!pip install fuzzywuzzy
from fuzzywuzzy import process
Collecting fuzzywuzzy Using cached fuzzywuzzy-0.18.0-py2.py3-none-any.whl (18 kB) Installing collected packages: fuzzywuzzy Successfully installed fuzzywuzzy-0.18.0
C:\ProgramData\Anaconda3\lib\site-packages\fuzzywuzzy\fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')
dfcols=df.columns
newdfcols=new_df.columns
matchednames =[]
for name in newdfcols:
matches = process.extractBests(name, colmap['Indicator name (alternative)'], score_cutoff=90)
if matches:
matched_name = matches[0][0]
mn_dict = {"name": name, "match": matched_name}
matchednames.append(mn_dict)
elif name == "Household Median Multiples": #fuzzywuzzy can't match this one
matched_name = "Median multiple for housing"
mn_dict = {"name": name, "match": matched_name}
matchednames.append(mn_dict)
elif name == "Gender Earnings Gap": #or this one I think
matched_name = "Gender pay gap"
mn_dict = {"name": name, "match": matched_name}
matchednames.append(mn_dict)
else:
print(name)
Unnamed: 0 Inadequacy Of Income, Gender Inadequacy Of Income, Housing Tenure Inadequacy Of Income, Long-Term Migrant Inadequacy Of Income, Māori Low Income, Gender
mndf= pd.DataFrame(matchednames)
len(matchednames)
102
dfcols;
newdfcols;
sum(mndf['match'].duplicated()) #make sure it hasn't matched many to one
0
newdfoldnames = mndf['name'].tolist()
newdfnewnames = mndf['match'].tolist()
new_df.rename(columns=dict(zip(newdfoldnames,newdfnewnames)), inplace=True)
colstodrop2=set(new_df.columns)-set(df.columns)
colstodrop2
{'Inadequacy Of Income, Gender',
'Inadequacy Of Income, Housing Tenure',
'Inadequacy Of Income, Long-Term Migrant',
'Inadequacy Of Income, Māori',
'Low Income, Gender',
'Unnamed: 0'}
That gender wage gap is a problem, that should be in both. let's see. (fixed now); the rest are indicators from sharedprosperit that don't appear in the provided dataset
#have tried modifying the fuzzywuzzy code above but risk mangling things further,
#will try just rename it in place just the one column
new_df.rename(columns={'Gender Wage Gap':'Gender pay gap'},inplace=True)
testdf=new_df.drop(columns=colstodrop2)
sum(testdf.columns.duplicated())
0
sum(df.columns.duplicated())
0
set(testdf.columns)-set(df.columns), set(df.columns)-set(testdf.columns) #checking to see if theres any difference
(set(), set())
#returns empty set so why is compare() not working?
set(df.columns) == set(testdf.columns)
True
df=df.apply(pd.to_numeric,errors='coerce') #because maybe its the datatype stopping it from comparing
df=df.astype(float)
df.columns.values, testdf.columns.values; #look the same; will try reindexing
cols = df.columns.tolist()
testdf = testdf.reindex(columns=cols) #ordering them the same to make it work
compdf=df.compare(testdf) #it worked
#One thing that's going on that I can't explain atm is that for the D10:D1-4 data it's dropped the first value and
#returned NaN whereas when I look at the data on plotly directly it's got a value of 0.91 for 1982. I'm going to
#fill in some of these values manually I think; it mostly looks right but there are a few gaps. Maybe there were
#errors with calling the plotly data. Another thing I haven't considered is that I think
#I called the first y trace, the interpolated values rather than the actual values, which is maybe better but then
#it's strange that there are still gaps. Given that I'm supposed to be doing interpolation as an exercise it's probably
#good in one sense to get the actual values and work on that but then part of the point of doing this was to
#complete a bunch of steps in one fell swoop and also teach myself some scraping skills and put them into practice.
#Given the time constraints I think I'll keep the interpolated data but I need to keep going through it and checking
#So for now I think what I'm gonna do is focus on these income and wealth indicators because these seem ripe for interpolation
#and also for searching for correlations and interesting relationships with other factors and it's expected
#I think that we use a limited subset of the data after cleaning because the datset is so rich it wouldn't
#necessarily be practical to analyse the entire set in this one assignment
compdf;
Ok it seems much better. I still need to have a look through, visual inspection of the graphs for the indicators on the website to see if my values match up better than the old ones but this seems promising. Now that I've spent three days figuring out how to do this I can try and get the rest of this finished.
One thing I noticed is that the loan delinquencies data has negative values. That doesn't seem right given that percentages generally aren't negative at least in the context of "How many loans are delinquent out of all loans?" I thought maybe it was people paying off loans that had been delinquent because of boom times in the early nineties or something. Anyway I tried to find the original series that was supposedly from rbnz but there was nothing there going back to 1992 and the only data they had about malperforming loans wasn't just personal loans which this data seems to be. I emailed and had a phone conversation with a senior data analyst at rbnz and they said it looks like this dataseries includes data from another source.
new_df['D10:D1-4 income share (Palma)'];
Ok actually that looks perfect. I'll try the same column from testdf
testdf['D10:D1-4 income share (Palma)'];
Seems the problem is only coming up in the compare function. Bothe new_df and testdf show the first value for 1982, it only shows up as NaN in the compare function.
print(df.equals(testdf))
False
#false; obviously there's huge differences.
goodnames = colmap['Indicator name']
badnames = colmap['Indicator name (alternative)']
new_df.rename(columns=dict(zip(badnames,goodnames)), inplace=True)
# replace "/" with "_"
new_df.columns = new_df.columns.str.replace("/", "_")
#replace : with to
new_df.columns = new_df.columns.str.replace(":", " to ")
new_df;
#gonna make scatter plots of the columns
#getting an error need to maybe reindex
#the error i think comes from the filenams; I think I'll rename the columns in this one to just
#indicator name which hopefully doesn't have forbidden characters
import matplotlib.pyplot as plt
import os
# create a directory to store the plots
os.makedirs("scatterplots", exist_ok=True)
# loop over each column in the dataframe
for col in new_df.columns:
# create a scatter plot
plt.scatter(new_df.index, new_df[col])
plt.xlabel("Index")
plt.ylabel(col)
plt.title(f"Scatterplot of {col}")
# save the plot to a file
plt.savefig(f"scatterplots/{col}.png")
# clear the plot for the next iteration
plt.clf()
<Figure size 640x480 with 0 Axes>
new_df.columns.values;
new_df.drop(columns='Unnamed to 0',inplace=True)
#idk i got a bunch of scatterplots and i wnat to compare them to the ones i downloaded from sharedprosperitt but now like
#those ones i downloaded are all saved as just the code numbers so i could programatically comper
#them but that would be relying ony my own index i created again so it wouldn't prove anything
#so what i might do is just compare them to the images on the website but it loads slow af because of all
#the fancy css so maybe I'll just screenshot the pages for quicker comparison later
I visually inspected the scatter plots and compared them to the screenshots from sharedprosperity.co.nz; they all matched although for the first two iterations there were problems with the housing category and the socioeconomic indicators; the number of indicators and codes was the same but the ordering was wrong and there was some duplication. I reordered the index I was using and just scraped all your plots all over again, deleted the scatterplots, remade them and then did it again once I came across the second set of errors. There is obviously a wide range of more sophisticated methods which wouldn't use anywhere near as much machine time but would take me longer to figure out.
We are now in a position to do actual analysis. The entire dataset is in new_df, plus the general inequality indicators which were previously left out. It's all as good as your own interpolation work. My plan is to use the income and wealth indicators as causal variables and test them against various other indicators and see how these things have changed over time because we have limited time left. I may try and include some more data and especially if I get time and more information to clean up the loan delinquency percentage data or determine why it's not looking like what I'd expect.
We also probably need to do some actual data wrangling rather than just stealing your figure interpolated data wholesale. There are still some gaps; at least the gaps are now NaN and able to be processed.